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2.497.442.544 1.134.938 2.344.938 1.573 0 2.292-.337 2.657-.751.384-.435.558-1.15.558-2.361 0-1.14-.243-1.847-.705-2.319-.477-.488-1.319-.862-2.824-1.025-1.487-.161-2.192.138-2.533.529-.269.307-.437.808-.438 1.578v.021c0 .265.021.562.063.893Zm-1.626 0c.042-.331.063-.628.063-.894v-.02c-.001-.77-.169-1.271-.438-1.578-.341-.391-1.046-.69-2.533-.529-1.505.163-2.347.537-2.824 1.025-.462.472-.705 1.179-.705 2.319 0 1.211.175 1.926.558 2.361.365.414 1.084.751 2.657.751 1.21 0 1.902-.394 2.344-.938.475-.584.742-1.44.878-2.497Z"></path><path d="M14.5 14.25a1 1 0 0 1 1 1v2a1 1 0 0 1-2 0v-2a1 1 0 0 1 1-1Zm-5 0a1 1 0 0 1 1 1v2a1 1 0 0 1-2 0v-2a1 1 0 0 1 1-1Z"></path> </svg> <div> <div class="color-fg-default h4">GitHub Copilot</div> Write better code with AI </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" 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2h3.5a.75.75 0 0 1 0 1.5h-3.5a.25.25 0 0 0-.25.25v8.5c0 .138.112.25.25.25h8.5a.25.25 0 0 0 .25-.25v-3.5a.75.75 0 0 1 1.5 0v3.5A1.75 1.75 0 0 1 12.25 14h-8.5A1.75 1.75 0 0 1 2 12.25v-8.5C2 2.784 2.784 2 3.75 2Zm6.854-1h4.146a.25.25 0 0 1 .25.25v4.146a.25.25 0 0 1-.427.177L13.03 4.03 9.28 7.78a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042l3.75-3.75-1.543-1.543A.25.25 0 0 1 10.604 1Z"></path> </svg> </a></li> </ul> </div> </div> </div> </li> <li class="HeaderMenu-item position-relative flex-wrap flex-justify-between flex-items-center d-block d-lg-flex flex-lg-nowrap flex-lg-items-center js-details-container js-header-menu-item"> <button type="button" class="HeaderMenu-link border-0 width-full width-lg-auto px-0 px-lg-2 py-lg-2 no-wrap d-flex flex-items-center flex-justify-between js-details-target" aria-expanded="false"> Solutions <svg opacity="0.5" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon 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0 1 1.06 0Z"></path> </svg> </button> <div class="HeaderMenu-dropdown dropdown-menu rounded m-0 p-0 pt-2 pt-lg-4 position-relative position-lg-absolute left-0 left-lg-n3 pb-2 pb-lg-4 px-lg-4"> <div class="HeaderMenu-column"> <div class="border-bottom pb-3 pb-lg-0 pb-lg-3 mb-3 mb-lg-0 mb-lg-3"> <ul class="list-style-none f5" > <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description" data-analytics-event="{"location":"navbar","action":"github_sponsors","context":"open_source","tag":"link","label":"github_sponsors_link_open_source_navbar"}" href="/sponsors"> <div> <div class="color-fg-default h4">GitHub Sponsors</div> Fund open source developers </div> </a></li> </ul> </div> <div class="border-bottom pb-3 pb-lg-0 pb-lg-3 mb-3 mb-lg-0 mb-lg-3"> <ul class="list-style-none f5" > <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center 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role="listbox" class="ActionListWrap QueryBuilder-ListWrap" aria-label="Suggestions" data-action=" combobox-commit:query-builder#comboboxCommit mousedown:query-builder#resultsMousedown " data-target="query-builder.resultsList" data-persist-list=false id="query-builder-test-results" ></ul> </div> <div class="FormControl-inlineValidation" id="validation-337e149d-74f0-4a07-9eb7-d7cb6b36d322" hidden="hidden"> <span class="FormControl-inlineValidation--visual"> <svg aria-hidden="true" height="12" viewBox="0 0 12 12" version="1.1" width="12" data-view-component="true" class="octicon octicon-alert-fill"> <path d="M4.855.708c.5-.896 1.79-.896 2.29 0l4.675 8.351a1.312 1.312 0 0 1-1.146 1.954H1.33A1.313 1.313 0 0 1 .183 9.058ZM7 7V3H5v4Zm-1 3a1 1 0 1 0 0-2 1 1 0 0 0 0 2Z"></path> </svg> </span> <span></span> </div> </div> <div data-target="query-builder.screenReaderFeedback" aria-live="polite" aria-atomic="true" class="sr-only"></div> </query-builder></form> <div class="d-flex flex-row color-fg-muted px-3 text-small color-bg-default search-feedback-prompt"> <a target="_blank" href="https://docs.github.com/search-github/github-code-search/understanding-github-code-search-syntax" data-view-component="true" class="Link color-fg-accent text-normal ml-2">Search syntax tips</a> <div class="d-flex flex-1"></div> </div> </div> </div> </div> </modal-dialog></div> </div> <div data-action="click:qbsearch-input#retract" class="dark-backdrop position-fixed" hidden data-target="qbsearch-input.darkBackdrop"></div> <div class="color-fg-default"> <dialog-helper> <dialog data-target="qbsearch-input.feedbackDialog" data-action="close:qbsearch-input#handleDialogClose cancel:qbsearch-input#handleDialogClose" id="feedback-dialog" aria-modal="true" aria-labelledby="feedback-dialog-title" aria-describedby="feedback-dialog-description" data-view-component="true" class="Overlay Overlay-whenNarrow Overlay--size-medium Overlay--motion-scaleFade Overlay--disableScroll"> <div 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--></option></form><form id="code-search-feedback-form" data-turbo="false" action="/search/feedback" accept-charset="UTF-8" method="post"><input type="hidden" data-csrf="true" name="authenticity_token" value="zbmNEWelq3qvSA2s/adTV0EkKm1Bm2BTwReiYqp+aCT8WMi/YqARCV9h/+k3dJ813irs7x/iserSQC+GPOvoEQ==" /> <p>We read every piece of feedback, and take your input very seriously.</p> <textarea name="feedback" class="form-control width-full mb-2" style="height: 120px" id="feedback"></textarea> <input name="include_email" id="include_email" aria-label="Include my email address so I can be contacted" class="form-control mr-2" type="checkbox"> <label for="include_email" style="font-weight: normal">Include my email address so I can be contacted</label> </form></div> </scrollable-region> <div data-view-component="true" class="Overlay-footer Overlay-footer--alignEnd"> <button data-close-dialog-id="feedback-dialog" type="button" data-view-component="true" class="btn"> Cancel </button> <button 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data-target="custom-scopes.createCustomScopeForm"> <!-- '"` --><!-- </textarea></xmp> --></option></form><form id="custom-scopes-dialog-form" data-turbo="false" action="/search/custom_scopes" accept-charset="UTF-8" method="post"><input type="hidden" data-csrf="true" name="authenticity_token" value="UZYP9gkunphWzYKfq2Of6gYPy0PPrqfLBaoTuUAoOMRuquGwNFqFGpaBRjxcGBBaWjbHyKfcYWEsMwoRH8FmZQ==" /> <div data-target="custom-scopes.customScopesModalDialogFlash"></div> <input type="hidden" id="custom_scope_id" name="custom_scope_id" data-target="custom-scopes.customScopesIdField"> <div class="form-group"> <label for="custom_scope_name">Name</label> <auto-check src="/search/custom_scopes/check_name" required only-validate-on-blur="false"> <input type="text" name="custom_scope_name" id="custom_scope_name" data-target="custom-scopes.customScopesNameField" class="form-control" autocomplete="off" placeholder="github-ruby" required maxlength="50"> <input type="hidden" data-csrf="true" 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rel=\"nofollow\"\u003e\u003cimg src=\"https://github.com/codespaces/badge.svg\" alt=\"Open in GitHub Codespaces\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003ca href=\"https://github.com/NVIDIA/cccl/blob/main/CONTRIBUTING.md\"\u003eContributor Guide\u003c/a\u003e\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://github.com/NVIDIA/cccl/blob/main/.devcontainer/README.md\"\u003eDev Containers\u003c/a\u003e\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://discord.gg/nvidiadeveloper\" rel=\"nofollow\"\u003eDiscord\u003c/a\u003e\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://godbolt.org/z/x4G73af9a\" rel=\"nofollow\"\u003eGodbolt\u003c/a\u003e\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://github.com/orgs/NVIDIA/projects/6\"\u003eGitHub Project\u003c/a\u003e\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://nvidia.github.io/cccl\" rel=\"nofollow\"\u003eDocumentation\u003c/a\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCUDA Core Compute Libraries (CCCL)\u003c/h1\u003e\u003ca id=\"user-content-cuda-core-compute-libraries-cccl\" class=\"anchor\" aria-label=\"Permalink: CUDA Core Compute Libraries (CCCL)\" href=\"#cuda-core-compute-libraries-cccl\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eWelcome to the CUDA Core Compute Libraries (CCCL) where our mission is to make CUDA more delightful.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThis repository unifies three essential CUDA C++ libraries into a single, convenient repository:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"/NVIDIA/cccl/blob/main/thrust\"\u003eThrust\u003c/a\u003e (\u003ca href=\"https://github.com/nvidia/thrust\"\u003eformer repo\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/NVIDIA/cccl/blob/main/cub\"\u003eCUB\u003c/a\u003e (\u003ca href=\"https://github.com/nvidia/cub\"\u003eformer repo\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/NVIDIA/cccl/blob/main/libcudacxx\"\u003elibcudacxx\u003c/a\u003e (\u003ca href=\"https://github.com/nvidia/libcudacxx\"\u003eformer repo\u003c/a\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eThe goal of CCCL is to provide CUDA C++ developers with building blocks that make it easier to write safe and efficient code.\nBringing these libraries together streamlines your development process and broadens your ability to leverage the power of CUDA C++.\nFor more information about the decision to unify these projects, see the \u003ca href=\"https://github.com/NVIDIA/cccl/discussions/520\" data-hovercard-type=\"discussion\" data-hovercard-url=\"/NVIDIA/cccl/discussions/520/hovercard\"\u003eannouncement here\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eOverview\u003c/h2\u003e\u003ca id=\"user-content-overview\" class=\"anchor\" aria-label=\"Permalink: Overview\" href=\"#overview\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThe concept for the CUDA Core Compute Libraries (CCCL) grew organically out of the Thrust, CUB, and libcudacxx projects that were developed independently over the years with a similar goal: to provide high-quality, high-performance, and easy-to-use C++ abstractions for CUDA developers.\nNaturally, there was a lot of overlap among the three projects, and it became clear the community would be better served by unifying them into a single repository.\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eThrust\u003c/strong\u003e is the C++ parallel algorithms library which inspired the introduction of parallel algorithms to the C++ Standard Library. Thrust's high-level interface greatly enhances programmer productivity while enabling performance portability between GPUs and multicore CPUs via configurable backends that allow using multiple parallel programming frameworks (such as CUDA, TBB, and OpenMP).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eCUB\u003c/strong\u003e is a lower-level, CUDA-specific library designed for speed-of-light parallel algorithms across all GPU architectures. In addition to device-wide algorithms, it provides \u003cem\u003ecooperative algorithms\u003c/em\u003e like block-wide reduction and warp-wide scan, providing CUDA kernel developers with building blocks to create speed-of-light, custom kernels.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003elibcudacxx\u003c/strong\u003e is the CUDA C++ Standard Library. It provides an implementation of the C++ Standard Library that works in both host and device code. Additionally, it provides abstractions for CUDA-specific hardware features like synchronization primitives, cache control, atomics, and more.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eThe main goal of CCCL is to fill a similar role that the Standard C++ Library fills for Standard C++: provide general-purpose, speed-of-light tools to CUDA C++ developers, allowing them to focus on solving the problems that matter.\nUnifying these projects is the first step towards realizing that goal.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eExample\u003c/h2\u003e\u003ca id=\"user-content-example\" class=\"anchor\" aria-label=\"Permalink: Example\" href=\"#example\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThis is a simple example demonstrating the use of CCCL functionality from Thrust, CUB, and libcudacxx.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eIt shows how to use Thrust/CUB/libcudacxx to implement a simple parallel reduction kernel.\nEach thread block computes the sum of a subset of the array using \u003ccode\u003ecub::BlockReduce\u003c/code\u003e.\nThe sum of each block is then reduced to a single value using an atomic add via \u003ccode\u003ecuda::atomic_ref\u003c/code\u003e from libcudacxx.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eIt then shows how the same reduction can be done using Thrust's \u003ccode\u003ereduce\u003c/code\u003e algorithm and compares the results.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://godbolt.org/z/aMx4j9f4T\" rel=\"nofollow\"\u003eTry it live on Godbolt!\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-c++ notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"#include \u0026lt;thrust/execution_policy.h\u0026gt;\n#include \u0026lt;thrust/device_vector.h\u0026gt;\n#include \u0026lt;cub/block/block_reduce.cuh\u0026gt;\n#include \u0026lt;cuda/atomic\u0026gt;\n#include \u0026lt;cuda/cmath\u0026gt;\n#include \u0026lt;cuda/std/span\u0026gt;\n#include \u0026lt;cstdio\u0026gt;\n\ntemplate \u0026lt;int block_size\u0026gt;\n__global__ void reduce(cuda::std::span\u0026lt;int const\u0026gt; data, cuda::std::span\u0026lt;int\u0026gt; result) {\n using BlockReduce = cub::BlockReduce\u0026lt;int, block_size\u0026gt;;\n __shared__ typename BlockReduce::TempStorage temp_storage;\n\n int const index = threadIdx.x + blockIdx.x * blockDim.x;\n int sum = 0;\n if (index \u0026lt; data.size()) {\n sum += data[index];\n }\n sum = BlockReduce(temp_storage).Sum(sum);\n\n if (threadIdx.x == 0) {\n cuda::atomic_ref\u0026lt;int, cuda::thread_scope_device\u0026gt; atomic_result(result.front());\n atomic_result.fetch_add(sum, cuda::memory_order_relaxed);\n }\n}\n\nint main() {\n\n // Allocate and initialize input data\n int const N = 1000;\n thrust::device_vector\u0026lt;int\u0026gt; data(N);\n thrust::fill(data.begin(), data.end(), 1);\n\n // Allocate output data\n thrust::device_vector\u0026lt;int\u0026gt; kernel_result(1);\n\n // Compute the sum reduction of `data` using a custom kernel\n constexpr int block_size = 256;\n int const num_blocks = cuda::ceil_div(N, block_size);\n reduce\u0026lt;block_size\u0026gt;\u0026lt;\u0026lt;\u0026lt;num_blocks, block_size\u0026gt;\u0026gt;\u0026gt;(cuda::std::span\u0026lt;int const\u0026gt;(thrust::raw_pointer_cast(data.data()), data.size()),\n cuda::std::span\u0026lt;int\u0026gt;(thrust::raw_pointer_cast(kernel_result.data()), 1));\n\n auto const err = cudaDeviceSynchronize();\n if (err != cudaSuccess) {\n std::cout \u0026lt;\u0026lt; \u0026quot;Error: \u0026quot; \u0026lt;\u0026lt; cudaGetErrorString(err) \u0026lt;\u0026lt; std::endl;\n return -1;\n }\n\n int const custom_result = kernel_result[0];\n\n // Compute the same sum reduction using Thrust\n int const thrust_result = thrust::reduce(thrust::device, data.begin(), data.end(), 0);\n\n // Ensure the two solutions are identical\n std::printf(\u0026quot;Custom kernel sum: %d\\n\u0026quot;, custom_result);\n std::printf(\u0026quot;Thrust reduce sum: %d\\n\u0026quot;, thrust_result);\n assert(kernel_result[0] == thrust_result);\n return 0;\n}\"\u003e\u003cpre\u003e#\u003cspan class=\"pl-k\"\u003einclude\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0026lt;\u003c/span\u003ethrust/execution_policy.h\u003cspan class=\"pl-pds\"\u003e\u0026gt;\u003c/span\u003e\u003c/span\u003e\n#\u003cspan class=\"pl-k\"\u003einclude\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0026lt;\u003c/span\u003ethrust/device_vector.h\u003cspan class=\"pl-pds\"\u003e\u0026gt;\u003c/span\u003e\u003c/span\u003e\n#\u003cspan class=\"pl-k\"\u003einclude\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0026lt;\u003c/span\u003ecub/block/block_reduce.cuh\u003cspan class=\"pl-pds\"\u003e\u0026gt;\u003c/span\u003e\u003c/span\u003e\n#\u003cspan class=\"pl-k\"\u003einclude\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0026lt;\u003c/span\u003ecuda/atomic\u003cspan class=\"pl-pds\"\u003e\u0026gt;\u003c/span\u003e\u003c/span\u003e\n#\u003cspan class=\"pl-k\"\u003einclude\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0026lt;\u003c/span\u003ecuda/cmath\u003cspan class=\"pl-pds\"\u003e\u0026gt;\u003c/span\u003e\u003c/span\u003e\n#\u003cspan class=\"pl-k\"\u003einclude\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0026lt;\u003c/span\u003ecuda/std/span\u003cspan class=\"pl-pds\"\u003e\u0026gt;\u003c/span\u003e\u003c/span\u003e\n#\u003cspan class=\"pl-k\"\u003einclude\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0026lt;\u003c/span\u003ecstdio\u003cspan class=\"pl-pds\"\u003e\u0026gt;\u003c/span\u003e\u003c/span\u003e\n\n\u003cspan class=\"pl-k\"\u003etemplate \u003c/span\u003e\u0026lt;\u003cspan class=\"pl-k\"\u003eint\u003c/span\u003e block_size\u0026gt;\n__global__ \u003cspan class=\"pl-k\"\u003evoid\u003c/span\u003e \u003cspan class=\"pl-en\"\u003ereduce\u003c/span\u003e(cuda::std::span\u0026lt;\u003cspan class=\"pl-k\"\u003eint\u003c/span\u003e \u003cspan class=\"pl-k\"\u003econst\u003c/span\u003e\u0026gt; data, cuda::std::span\u0026lt;\u003cspan class=\"pl-k\"\u003eint\u003c/span\u003e\u0026gt; result) {\n \u003cspan class=\"pl-k\"\u003eusing\u003c/span\u003e BlockReduce = cub::BlockReduce\u0026lt;\u003cspan class=\"pl-k\"\u003eint\u003c/span\u003e, block_size\u0026gt;;\n __shared__ \u003cspan class=\"pl-k\"\u003etypename\u003c/span\u003e BlockReduce::TempStorage temp_storage;\n\n \u003cspan class=\"pl-k\"\u003eint\u003c/span\u003e \u003cspan class=\"pl-k\"\u003econst\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eindex\u003c/span\u003e = threadIdx.\u003cspan class=\"pl-smi\"\u003ex\u003c/span\u003e + blockIdx.\u003cspan class=\"pl-smi\"\u003ex\u003c/span\u003e * blockDim.\u003cspan class=\"pl-smi\"\u003ex\u003c/span\u003e;\n \u003cspan class=\"pl-k\"\u003eint\u003c/span\u003e sum = \u003cspan class=\"pl-c1\"\u003e0\u003c/span\u003e;\n \u003cspan class=\"pl-k\"\u003eif\u003c/span\u003e (\u003cspan class=\"pl-c1\"\u003eindex\u003c/span\u003e \u0026lt; data.\u003cspan class=\"pl-c1\"\u003esize\u003c/span\u003e()) {\n sum += data[\u003cspan class=\"pl-c1\"\u003eindex\u003c/span\u003e];\n }\n sum = \u003cspan class=\"pl-c1\"\u003eBlockReduce\u003c/span\u003e(temp_storage).\u003cspan class=\"pl-c1\"\u003eSum\u003c/span\u003e(sum);\n\n \u003cspan class=\"pl-k\"\u003eif\u003c/span\u003e (threadIdx.\u003cspan class=\"pl-smi\"\u003ex\u003c/span\u003e == \u003cspan class=\"pl-c1\"\u003e0\u003c/span\u003e) {\n cuda::atomic_ref\u0026lt;\u003cspan class=\"pl-k\"\u003eint\u003c/span\u003e, cuda::thread_scope_device\u0026gt; \u003cspan class=\"pl-c1\"\u003eatomic_result\u003c/span\u003e(result.\u003cspan class=\"pl-c1\"\u003efront\u003c/span\u003e());\n atomic_result.\u003cspan class=\"pl-c1\"\u003efetch_add\u003c/span\u003e(sum, cuda::memory_order_relaxed);\n }\n}\n\n\u003cspan class=\"pl-k\"\u003eint\u003c/span\u003e \u003cspan class=\"pl-en\"\u003emain\u003c/span\u003e() {\n\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e//\u003c/span\u003e Allocate and initialize input data\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003eint\u003c/span\u003e \u003cspan class=\"pl-k\"\u003econst\u003c/span\u003e N = \u003cspan class=\"pl-c1\"\u003e1000\u003c/span\u003e;\n thrust::device_vector\u0026lt;\u003cspan class=\"pl-k\"\u003eint\u003c/span\u003e\u0026gt; \u003cspan class=\"pl-c1\"\u003edata\u003c/span\u003e(N);\n \u003cspan class=\"pl-c1\"\u003ethrust::fill\u003c/span\u003e(data.\u003cspan class=\"pl-c1\"\u003ebegin\u003c/span\u003e(), data.\u003cspan class=\"pl-c1\"\u003eend\u003c/span\u003e(), \u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e);\n\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e//\u003c/span\u003e Allocate output data\u003c/span\u003e\n thrust::device_vector\u0026lt;\u003cspan class=\"pl-k\"\u003eint\u003c/span\u003e\u0026gt; \u003cspan class=\"pl-c1\"\u003ekernel_result\u003c/span\u003e(\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e);\n\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e//\u003c/span\u003e Compute the sum reduction of `data` using a custom kernel\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003econstexpr\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eint\u003c/span\u003e block_size = \u003cspan class=\"pl-c1\"\u003e256\u003c/span\u003e;\n \u003cspan class=\"pl-k\"\u003eint\u003c/span\u003e \u003cspan class=\"pl-k\"\u003econst\u003c/span\u003e num_blocks = \u003cspan class=\"pl-c1\"\u003ecuda::ceil_div\u003c/span\u003e(N, block_size);\n reduce\u0026lt;block_size\u0026gt;\u0026lt;\u0026lt;\u0026lt;num_blocks, block_size\u0026gt;\u0026gt;\u0026gt;(cuda::std::span\u0026lt;\u003cspan class=\"pl-k\"\u003eint\u003c/span\u003e \u003cspan class=\"pl-k\"\u003econst\u003c/span\u003e\u0026gt;(\u003cspan class=\"pl-c1\"\u003ethrust::raw_pointer_cast\u003c/span\u003e(data.\u003cspan class=\"pl-c1\"\u003edata\u003c/span\u003e()), data.\u003cspan class=\"pl-c1\"\u003esize\u003c/span\u003e()),\n cuda::std::span\u0026lt;\u003cspan class=\"pl-k\"\u003eint\u003c/span\u003e\u0026gt;(\u003cspan class=\"pl-c1\"\u003ethrust::raw_pointer_cast\u003c/span\u003e(kernel_result.\u003cspan class=\"pl-c1\"\u003edata\u003c/span\u003e()), \u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e));\n\n \u003cspan class=\"pl-k\"\u003eauto\u003c/span\u003e \u003cspan class=\"pl-k\"\u003econst\u003c/span\u003e err = \u003cspan class=\"pl-c1\"\u003ecudaDeviceSynchronize\u003c/span\u003e();\n \u003cspan class=\"pl-k\"\u003eif\u003c/span\u003e (err != cudaSuccess) {\n std::cout \u0026lt;\u0026lt; \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eError: \u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u0026lt;\u0026lt; \u003cspan class=\"pl-c1\"\u003ecudaGetErrorString\u003c/span\u003e(err) \u0026lt;\u0026lt; std::endl;\n \u003cspan class=\"pl-k\"\u003ereturn\u003c/span\u003e -\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e;\n }\n\n \u003cspan class=\"pl-k\"\u003eint\u003c/span\u003e \u003cspan class=\"pl-k\"\u003econst\u003c/span\u003e custom_result = kernel_result[\u003cspan class=\"pl-c1\"\u003e0\u003c/span\u003e];\n\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e//\u003c/span\u003e Compute the same sum reduction using Thrust\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003eint\u003c/span\u003e \u003cspan class=\"pl-k\"\u003econst\u003c/span\u003e thrust_result = \u003cspan class=\"pl-c1\"\u003ethrust::reduce\u003c/span\u003e(thrust::device, data.\u003cspan class=\"pl-c1\"\u003ebegin\u003c/span\u003e(), data.\u003cspan class=\"pl-c1\"\u003eend\u003c/span\u003e(), \u003cspan class=\"pl-c1\"\u003e0\u003c/span\u003e);\n\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e//\u003c/span\u003e Ensure the two solutions are identical\u003c/span\u003e\n \u003cspan class=\"pl-c1\"\u003estd::printf\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eCustom kernel sum: %d\u003cspan class=\"pl-cce\"\u003e\\n\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, custom_result);\n \u003cspan class=\"pl-c1\"\u003estd::printf\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eThrust reduce sum: %d\u003cspan class=\"pl-cce\"\u003e\\n\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e, thrust_result);\n \u003cspan class=\"pl-c1\"\u003eassert\u003c/span\u003e(kernel_result[\u003cspan class=\"pl-c1\"\u003e0\u003c/span\u003e] == thrust_result);\n \u003cspan class=\"pl-k\"\u003ereturn\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0\u003c/span\u003e;\n}\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eGetting Started\u003c/h2\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-label=\"Permalink: Getting Started\" href=\"#getting-started\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eUsers\u003c/h3\u003e\u003ca id=\"user-content-users\" class=\"anchor\" aria-label=\"Permalink: Users\" href=\"#users\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eEverything in CCCL is header-only.\nTherefore, users need only concern themselves with how they get the header files and how they incorporate them into their build system.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCUDA Toolkit\u003c/h4\u003e\u003ca id=\"user-content-cuda-toolkit\" class=\"anchor\" aria-label=\"Permalink: CUDA Toolkit\" href=\"#cuda-toolkit\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThe easiest way to get started using CCCL is via the \u003ca href=\"https://developer.nvidia.com/cuda-toolkit\" rel=\"nofollow\"\u003eCUDA Toolkit\u003c/a\u003e which includes the CCCL headers.\nWhen you compile with \u003ccode\u003envcc\u003c/code\u003e, it automatically adds CCCL headers to your include path so you can simply \u003ccode\u003e#include\u003c/code\u003e any CCCL header in your code with no additional configuration required.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eIf compiling with another compiler, you will need to update your build system's include search path to point to the CCCL headers in your CTK install (e.g., \u003ccode\u003e/usr/local/cuda/include\u003c/code\u003e).\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-c++ notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"#include \u0026lt;thrust/device_vector.h\u0026gt;\n#include \u0026lt;cub/cub.cuh\u0026gt;\n#include \u0026lt;cuda/std/atomic\u0026gt;\"\u003e\u003cpre\u003e#\u003cspan class=\"pl-k\"\u003einclude\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0026lt;\u003c/span\u003ethrust/device_vector.h\u003cspan class=\"pl-pds\"\u003e\u0026gt;\u003c/span\u003e\u003c/span\u003e\n#\u003cspan class=\"pl-k\"\u003einclude\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0026lt;\u003c/span\u003ecub/cub.cuh\u003cspan class=\"pl-pds\"\u003e\u0026gt;\u003c/span\u003e\u003c/span\u003e\n#\u003cspan class=\"pl-k\"\u003einclude\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0026lt;\u003c/span\u003ecuda/std/atomic\u003cspan class=\"pl-pds\"\u003e\u0026gt;\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eGitHub\u003c/h4\u003e\u003ca id=\"user-content-github\" class=\"anchor\" aria-label=\"Permalink: GitHub\" href=\"#github\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eUsers who want to stay on the cutting edge of CCCL development are encouraged to use CCCL from GitHub.\nUsing a newer version of CCCL with an older version of the CUDA Toolkit is supported, but not the other way around.\nFor complete information on compatibility between CCCL and the CUDA Toolkit, see \u003ca href=\"#platform-support\"\u003eour platform support\u003c/a\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eEverything in CCCL is header-only, so cloning and including it in a simple project is as easy as the following:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"git clone https://github.com/NVIDIA/cccl.git\nnvcc -Icccl/thrust -Icccl/libcudacxx/include -Icccl/cub main.cu -o main\"\u003e\u003cpre\u003egit clone https://github.com/NVIDIA/cccl.git\nnvcc -Icccl/thrust -Icccl/libcudacxx/include -Icccl/cub main.cu -o main\u003c/pre\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eNote\u003c/strong\u003e\nUse \u003ccode\u003e-I\u003c/code\u003e and not \u003ccode\u003e-isystem\u003c/code\u003e to avoid collisions with the CCCL headers implicitly included by \u003ccode\u003envcc\u003c/code\u003e from the CUDA Toolkit. All CCCL headers use \u003ccode\u003e#pragma system_header\u003c/code\u003e to ensure warnings will still be silenced as if using \u003ccode\u003e-isystem\u003c/code\u003e, see \u003ca class=\"issue-link js-issue-link\" data-error-text=\"Failed to load title\" data-id=\"1930979977\" data-permission-text=\"Title is private\" data-url=\"https://github.com/NVIDIA/cccl/issues/527\" data-hovercard-type=\"issue\" data-hovercard-url=\"/NVIDIA/cccl/issues/527/hovercard\" href=\"https://github.com/NVIDIA/cccl/issues/527\"\u003e#527\u003c/a\u003e for more information.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch5 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eInstallation\u003c/h5\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eA minimal build that only generates installation rules can be configured using the \u003ccode\u003einstall\u003c/code\u003e CMake preset:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"git clone https://github.com/NVIDIA/cccl.git\ncd cccl\ncmake --preset install -DCMAKE_INSTALL_PREFIX=/usr/local/\ncd build/install\nninja install\"\u003e\u003cpre\u003egit clone https://github.com/NVIDIA/cccl.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e cccl\ncmake --preset install -DCMAKE_INSTALL_PREFIX=/usr/local/\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e build/install\nninja install\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eTo include experimental libraries in the installation, use the \u003ccode\u003einstall-unstable\u003c/code\u003e preset and build directory.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eTo install \u003cstrong\u003eonly\u003c/strong\u003e the experimental libraries, use the \u003ccode\u003einstall-unstable-only\u003c/code\u003e preset and build directory.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eConda\u003c/h4\u003e\u003ca id=\"user-content-conda\" class=\"anchor\" aria-label=\"Permalink: Conda\" href=\"#conda\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eCCCL also provides conda packages of each release via the \u003ccode\u003econda-forge\u003c/code\u003e channel:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"conda config --add channels conda-forge\nconda install cccl\"\u003e\u003cpre\u003econda config --add channels conda-forge\nconda install cccl\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThis will install the latest CCCL to the conda environment's \u003ccode\u003e$CONDA_PREFIX/include/\u003c/code\u003e and \u003ccode\u003e$CONDA_PREFIX/lib/cmake/\u003c/code\u003e directories.\nIt is discoverable by CMake via \u003ccode\u003efind_package(CCCL)\u003c/code\u003e and can be used by any compilers in the conda environment.\nFor more information, see \u003ca href=\"https://conda-forge.org/docs/user/introduction/\" rel=\"nofollow\"\u003ethis introduction to conda-forge\u003c/a\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eIf you want to use the same CCCL version that shipped with a particular CUDA Toolkit, e.g. CUDA 12.4, you can install CCCL with:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"conda config --add channels conda-forge\nconda install cuda-cccl cuda-version=12.4\"\u003e\u003cpre\u003econda config --add channels conda-forge\nconda install cuda-cccl cuda-version=12.4\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThe \u003ccode\u003ecuda-cccl\u003c/code\u003e metapackage installs the \u003ccode\u003ecccl\u003c/code\u003e version that shipped with the CUDA Toolkit corresponding to \u003ccode\u003ecuda-version\u003c/code\u003e.\nIf you wish to update to the latest \u003ccode\u003ecccl\u003c/code\u003e after installing \u003ccode\u003ecuda-cccl\u003c/code\u003e, uninstall \u003ccode\u003ecuda-cccl\u003c/code\u003e before updating \u003ccode\u003ecccl\u003c/code\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"conda uninstall cuda-cccl\nconda install -c conda-forge cccl\"\u003e\u003cpre\u003econda uninstall cuda-cccl\nconda install -c conda-forge cccl\u003c/pre\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eNote\u003c/strong\u003e\nThere are also conda packages with names like \u003ccode\u003ecuda-cccl_linux-64\u003c/code\u003e.\nThose packages contain the CCCL versions shipped as part of the CUDA Toolkit, but are designed for internal use by the CUDA Toolkit.\nInstall \u003ccode\u003ecccl\u003c/code\u003e or \u003ccode\u003ecuda-cccl\u003c/code\u003e instead, for compatibility with conda compilers.\nFor more information, see the \u003ca href=\"https://github.com/conda-forge/cccl-feedstock/blob/main/recipe/meta.yaml\"\u003ecccl conda-forge recipe\u003c/a\u003e.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch5 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCMake Integration\u003c/h5\u003e\u003ca id=\"user-content-cmake-integration\" class=\"anchor\" aria-label=\"Permalink: CMake Integration\" href=\"#cmake-integration\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eCCCL uses \u003ca href=\"https://cmake.org/\" rel=\"nofollow\"\u003eCMake\u003c/a\u003e for all build and installation infrastructure, including tests as well as targets to link against in other CMake projects.\nTherefore, CMake is the recommended way to integrate CCCL into another project.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eFor a complete example of how to do this using CMake Package Manager see \u003ca href=\"/NVIDIA/cccl/blob/main/examples/basic\"\u003eour basic example project\u003c/a\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eOther build systems should work, but only CMake is tested.\nContributions to simplify integrating CCCL into other build systems are welcome.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eContributors\u003c/h3\u003e\u003ca id=\"user-content-contributors\" class=\"anchor\" aria-label=\"Permalink: Contributors\" href=\"#contributors\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eInterested in contributing to making CCCL better? Check out our \u003ca href=\"/NVIDIA/cccl/blob/main/CONTRIBUTING.md\"\u003eContributing Guide\u003c/a\u003e for a comprehensive overview of everything you need to know to set up your development environment, make changes, run tests, and submit a PR.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003ePlatform Support\u003c/h2\u003e\u003ca id=\"user-content-platform-support\" class=\"anchor\" aria-label=\"Permalink: Platform Support\" href=\"#platform-support\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eObjective:\u003c/strong\u003e This section describes where users can expect CCCL to compile and run successfully.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eIn general, CCCL should work everywhere the CUDA Toolkit is supported, however, the devil is in the details.\nThe sections below describe the details of support and testing for different versions of the CUDA Toolkit, host compilers, and C++ dialects.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCUDA Toolkit (CTK) Compatibility\u003c/h3\u003e\u003ca id=\"user-content-cuda-toolkit-ctk-compatibility\" class=\"anchor\" aria-label=\"Permalink: CUDA Toolkit (CTK) Compatibility\" href=\"#cuda-toolkit-ctk-compatibility\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eSummary:\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eThe latest version of CCCL is backward compatible with the current and preceding CTK major version series\u003c/li\u003e\n\u003cli\u003eCCCL is never forward compatible with any version of the CTK. Always use the same or newer than what is included with your CTK.\u003c/li\u003e\n\u003cli\u003eMinor version CCCL upgrades won't break existing code, but new features may not support all CTK versions\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eCCCL users are encouraged to capitalize on the latest enhancements and \u003ca href=\"https://www.youtube.com/watch?v=tISy7EJQPzI\" rel=\"nofollow\"\u003e\"live at head\"\u003c/a\u003e by always using the newest version of CCCL.\nFor a seamless experience, you can upgrade CCCL independently of the entire CUDA Toolkit.\nThis is possible because CCCL maintains backward compatibility with the latest patch release of every minor CTK release from both the current and previous major version series.\nIn some exceptional cases, the minimum supported minor version of the CUDA Toolkit release may need to be newer than the oldest release within its major version series.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eWhen a new major CTK is released, we drop support for the oldest supported major version.\u003c/p\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eCCCL Version\u003c/th\u003e\n\u003cth\u003eSupports CUDA Toolkit Version\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e2.x\u003c/td\u003e\n\u003ctd\u003e11.1 - 11.8, 12.x (only latest patch releases)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e3.x\u003c/td\u003e\n\u003ctd\u003e12.x, 13.x (only latest patch releases)\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"#compatibility-guidelines\"\u003eWell-behaved code\u003c/a\u003e using the latest CCCL should compile and run successfully with any supported CTK version.\nExceptions may occur for new features that depend on new CTK features, so those features would not work on older versions of the CTK.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eUsers can integrate a newer version of CCCL into an older CTK, but not the other way around.\nThis means an older version of CCCL is not compatible with a newer CTK.\nIn other words, \u003cstrong\u003eCCCL is never forward compatible with the CUDA Toolkit.\u003c/strong\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThe table below summarizes compatibility of the CTK and CCCL:\u003c/p\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003eCTK Version\u003c/th\u003e\n\u003cth align=\"center\"\u003eIncluded CCCL Version\u003c/th\u003e\n\u003cth align=\"center\"\u003eDesired CCCL\u003c/th\u003e\n\u003cth align=\"center\"\u003eSupported?\u003c/th\u003e\n\u003cth align=\"center\"\u003eNotes\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eCTK \u003ccode\u003eX.Y\u003c/code\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCCCL \u003ccode\u003eMAJOR.MINOR\u003c/code\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCCCL \u003ccode\u003eMAJOR.MINOR+n\u003c/code\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e✅\u003c/td\u003e\n\u003ctd align=\"center\"\u003eSome new features might not work\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eCTK \u003ccode\u003eX.Y\u003c/code\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCCCL \u003ccode\u003eMAJOR.MINOR\u003c/code\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCCCL \u003ccode\u003eMAJOR+1.MINOR\u003c/code\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e✅\u003c/td\u003e\n\u003ctd align=\"center\"\u003ePossible breaks; some new features might not be available\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eCTK \u003ccode\u003eX.Y\u003c/code\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCCCL \u003ccode\u003eMAJOR.MINOR\u003c/code\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCCCL \u003ccode\u003eMAJOR+2.MINOR\u003c/code\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e❌\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCCCL supports only two CTK major versions\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eCTK \u003ccode\u003eX.Y\u003c/code\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCCCL \u003ccode\u003eMAJOR.MINOR\u003c/code\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCCCL \u003ccode\u003eMAJOR.MINOR-n\u003c/code\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e❌\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCCCL isn't forward compatible\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eCTK \u003ccode\u003eX.Y\u003c/code\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCCCL \u003ccode\u003eMAJOR.MINOR\u003c/code\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCCCL \u003ccode\u003eMAJOR-n.MINOR\u003c/code\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e❌\u003c/td\u003e\n\u003ctd align=\"center\"\u003eCCCL isn't forward compatible\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cp dir=\"auto\"\u003eFor more information on CCCL versioning, API/ABI compatibility, and breaking changes see the \u003ca href=\"#versioning\"\u003eVersioning\u003c/a\u003e section below.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eOperating Systems\u003c/h3\u003e\u003ca id=\"user-content-operating-systems\" class=\"anchor\" aria-label=\"Permalink: Operating Systems\" href=\"#operating-systems\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eUnless otherwise specified, CCCL supports all the same operating systems as the CUDA Toolkit, which are documented here:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#system-requirements\" rel=\"nofollow\"\u003eLinux\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://docs.nvidia.com/cuda/cuda-installation-guide-microsoft-windows/index.html#system-requirements\" rel=\"nofollow\"\u003eWindows\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eHost Compilers\u003c/h3\u003e\u003ca id=\"user-content-host-compilers\" class=\"anchor\" aria-label=\"Permalink: Host Compilers\" href=\"#host-compilers\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eUnless otherwise specified, CCCL supports the same host compilers as the latest CUDA Toolkit, which are documented here:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#host-compiler-support-policy\" rel=\"nofollow\"\u003eLinux\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://docs.nvidia.com/cuda/cuda-installation-guide-microsoft-windows/index.html#system-requirements\" rel=\"nofollow\"\u003eWindows\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eFor GCC on Linux, at least 7.x is required.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eWhen using older CUDA Toolkits, we also only support the host compilers of the latest CUDA Toolkit,\nbut at least the most recent host compiler of any supported older CUDA Toolkit.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eWe may retain support of additional compilers and will accept corresponding patches from the community with reasonable fixes.\nBut we will not invest significant time in triaging or fixing issues for older compilers.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eIn the spirit of \"You only support what you test\", see our \u003ca href=\"https://github.com/NVIDIA/cccl/blob/main/ci-overview.md\"\u003eCI Overview\u003c/a\u003e for more information on exactly what we test.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eC++ Dialects\u003c/h3\u003e\u003ca id=\"user-content-c-dialects\" class=\"anchor\" aria-label=\"Permalink: C++ Dialects\" href=\"#c-dialects\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eC++17\u003c/li\u003e\n\u003cli\u003eC++20\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eGPU Architectures\u003c/h3\u003e\u003ca id=\"user-content-gpu-architectures\" class=\"anchor\" aria-label=\"Permalink: GPU Architectures\" href=\"#gpu-architectures\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eUnless otherwise specified, CCCL supports all the same GPU architectures/Compute Capabilities as the CUDA Toolkit, which are documented here: \u003ca href=\"https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#compute-capability\" rel=\"nofollow\"\u003ehttps://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#compute-capability\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eNote that some features may only support certain architectures/Compute Capabilities.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eTesting Strategy\u003c/h3\u003e\u003ca id=\"user-content-testing-strategy\" class=\"anchor\" aria-label=\"Permalink: Testing Strategy\" href=\"#testing-strategy\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eCCCL's testing strategy strikes a balance between testing as many configurations as possible and maintaining reasonable CI times.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eFor CUDA Toolkit versions, testing is done against both the oldest and the newest supported versions.\nFor instance, if the latest version of the CUDA Toolkit is 12.6, tests are conducted against 11.1 and 12.6.\nFor each CUDA version, builds are completed against all supported host compilers with all supported C++ dialects.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThe testing strategy and matrix are constantly evolving.\nThe matrix defined in the \u003ca href=\"/NVIDIA/cccl/blob/main/ci/matrix.yaml\"\u003e\u003ccode\u003eci/matrix.yaml\u003c/code\u003e\u003c/a\u003e file is the definitive source of truth.\nFor more information about our CI pipeline, see \u003ca href=\"/NVIDIA/cccl/blob/main/ci-overview.md\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eVersioning\u003c/h2\u003e\u003ca id=\"user-content-versioning\" class=\"anchor\" aria-label=\"Permalink: Versioning\" href=\"#versioning\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eObjective:\u003c/strong\u003e This section describes how CCCL is versioned, API/ABI stability guarantees, and compatibility guidelines to minimize upgrade headaches.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eSummary\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eThe entirety of CCCL's API shares a common semantic version across all components\u003c/li\u003e\n\u003cli\u003eOnly the most recently released version is supported and fixes are not backported to prior releases\u003c/li\u003e\n\u003cli\u003eAPI breaking changes and incrementing CCCL's major version will only coincide with a new major version release of the CUDA Toolkit\u003c/li\u003e\n\u003cli\u003eNot all source breaking changes are considered breaking changes of the public API that warrant bumping the major version number\u003c/li\u003e\n\u003cli\u003eDo not rely on ABI stability of entities in the \u003ccode\u003ecub::\u003c/code\u003e or \u003ccode\u003ethrust::\u003c/code\u003e namespaces\u003c/li\u003e\n\u003cli\u003eABI breaking changes for symbols in the \u003ccode\u003ecuda::\u003c/code\u003e namespace may happen at any time, but will be reflected by incrementing the ABI version which is embedded in an inline namespace for all \u003ccode\u003ecuda::\u003c/code\u003e symbols. Multiple ABI versions may be supported concurrently.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eNote:\u003c/strong\u003e Prior to merging Thrust, CUB, and libcudacxx into this repository, each library was independently versioned according to semantic versioning.\nStarting with the 2.1 release, all three libraries synchronized their release versions in their separate repositories.\nMoving forward, CCCL will continue to be released under a single \u003ca href=\"https://semver.org/\" rel=\"nofollow\"\u003esemantic version\u003c/a\u003e, with 2.2.0 being the first release from the \u003ca href=\"/NVIDIA/cccl/blob/main/www.github.com/nvidia/cccl\"\u003envidia/cccl\u003c/a\u003e repository.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eBreaking Change\u003c/h3\u003e\u003ca id=\"user-content-breaking-change\" class=\"anchor\" aria-label=\"Permalink: Breaking Change\" href=\"#breaking-change\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eA Breaking Change is a change to \u003cstrong\u003eexplicitly supported\u003c/strong\u003e functionality between released versions that would require a user to do work in order to upgrade to the newer version.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eIn the limit, \u003ca href=\"https://www.hyrumslaw.com/\" rel=\"nofollow\"\u003e\u003cem\u003eany\u003c/em\u003e change\u003c/a\u003e has the potential to break someone somewhere.\nAs a result, not all possible source breaking changes are considered Breaking Changes to the public API that warrant bumping the major semantic version.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThe sections below describe the details of breaking changes to CCCL's API and ABI.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eApplication Programming Interface (API)\u003c/h3\u003e\u003ca id=\"user-content-application-programming-interface-api\" class=\"anchor\" aria-label=\"Permalink: Application Programming Interface (API)\" href=\"#application-programming-interface-api\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eCCCL's public API is the entirety of the functionality \u003cem\u003eintentionally\u003c/em\u003e exposed to provide the utility of the library.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eIn other words, CCCL's public API goes beyond just function signatures and includes (but is not limited to):\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eThe location and names of headers intended for direct inclusion in user code\u003c/li\u003e\n\u003cli\u003eThe namespaces intended for direct use in user code\u003c/li\u003e\n\u003cli\u003eThe declarations and/or definitions of functions, classes, and variables located in headers and intended for direct use in user code\u003c/li\u003e\n\u003cli\u003eThe semantics of functions, classes, and variables intended for direct use in user code\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eMoreover, CCCL's public API does \u003cstrong\u003enot\u003c/strong\u003e include any of the following:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eAny symbol prefixed with \u003ccode\u003e_\u003c/code\u003e or \u003ccode\u003e__\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003eAny symbol whose name contains \u003ccode\u003edetail\u003c/code\u003e including the \u003ccode\u003edetail::\u003c/code\u003e namespace or a macro\u003c/li\u003e\n\u003cli\u003eAny header file contained in a \u003ccode\u003edetail/\u003c/code\u003e directory or sub-directory thereof\u003c/li\u003e\n\u003cli\u003eThe header files implicitly included by any header part of the public API\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eIn general, the goal is to avoid breaking anything in the public API.\nSuch changes are made only if they offer users better performance, easier-to-understand APIs, and/or more consistent APIs.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eAny breaking change to the public API will require bumping CCCL's major version number.\nIn keeping with \u003ca href=\"https://docs.nvidia.com/deploy/cuda-compatibility/#minor-version-compatibility\" rel=\"nofollow\"\u003eCUDA Minor Version Compatibility\u003c/a\u003e,\nAPI breaking changes and CCCL major version bumps will only occur coinciding with a new major version release of the CUDA Toolkit.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eAnything not part of the public API may change at any time without warning.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eAPI Versioning\u003c/h4\u003e\u003ca id=\"user-content-api-versioning\" class=\"anchor\" aria-label=\"Permalink: API Versioning\" href=\"#api-versioning\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThe public API of all CCCL's components share a unified semantic version of \u003ccode\u003eMAJOR.MINOR.PATCH\u003c/code\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eOnly the most recently released version is supported.\nAs a rule, features and bug fixes are not backported to previously released version or branches.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThe preferred method for querying the version is to use \u003ccode\u003eCCCL_[MAJOR/MINOR/PATCH_]VERSION\u003c/code\u003e as described below.\nFor backwards compatibility, the Thrust/CUB/libcudacxxx version definitions are available and will always be consistent with \u003ccode\u003eCCCL_VERSION\u003c/code\u003e.\nNote that Thrust/CUB use a \u003ccode\u003eMMMmmmpp\u003c/code\u003e scheme whereas the CCCL and libcudacxx use \u003ccode\u003eMMMmmmppp\u003c/code\u003e.\u003c/p\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003c/th\u003e\n\u003cth\u003eCCCL\u003c/th\u003e\n\u003cth\u003elibcudacxx\u003c/th\u003e\n\u003cth\u003eThrust\u003c/th\u003e\n\u003cth\u003eCUB\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eHeader\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e\u0026lt;cuda/version\u0026gt;\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e\u0026lt;cuda/std/version\u0026gt;\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e\u0026lt;thrust/version.h\u0026gt;\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e\u0026lt;cub/version.h\u0026gt;\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMajor Version\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eCCCL_MAJOR_VERSION\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e_LIBCUDACXX_CUDA_API_VERSION_MAJOR\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eTHRUST_MAJOR_VERSION\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eCUB_MAJOR_VERSION\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMinor Version\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eCCCL_MINOR_VERSION\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e_LIBCUDACXX_CUDA_API_VERSION_MINOR\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eTHRUST_MINOR_VERSION\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eCUB_MINOR_VERSION\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePatch/Subminor Version\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eCCCL_PATCH_VERSION\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e_LIBCUDACXX_CUDA_API_VERSION_PATCH\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eTHRUST_SUBMINOR_VERSION\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eCUB_SUBMINOR_VERSION\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eConcatenated Version\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eCCCL_VERSION (MMMmmmppp)\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e_LIBCUDACXX_CUDA_API_VERSION (MMMmmmppp)\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eTHRUST_VERSION (MMMmmmpp)\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eCUB_VERSION (MMMmmmpp)\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eApplication Binary Interface (ABI)\u003c/h3\u003e\u003ca id=\"user-content-application-binary-interface-abi\" class=\"anchor\" aria-label=\"Permalink: Application Binary Interface (ABI)\" href=\"#application-binary-interface-abi\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThe Application Binary Interface (ABI) is a set of rules for:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eHow a library's components are represented in machine code\u003c/li\u003e\n\u003cli\u003eHow those components interact across different translation units\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eA library's ABI includes, but is not limited to:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eThe mangled names of functions and types\u003c/li\u003e\n\u003cli\u003eThe size and alignment of objects and types\u003c/li\u003e\n\u003cli\u003eThe semantics of the bytes in the binary representation of an object\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eAn \u003cstrong\u003eABI Breaking Change\u003c/strong\u003e is any change that results in a change to the ABI of a function or type in the public API.\nFor example, adding a new data member to a struct is an ABI Breaking Change as it changes the size of the type.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eIn CCCL, the guarantees about ABI are as follows:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eSymbols in the \u003ccode\u003ethrust::\u003c/code\u003e and \u003ccode\u003ecub::\u003c/code\u003e namespaces may break ABI at any time without warning.\u003c/li\u003e\n\u003cli\u003eThe ABI of \u003ccode\u003ethrust::\u003c/code\u003e and \u003ccode\u003ecub::\u003c/code\u003e \u003ca href=\"https://nvidia.github.io/cccl/cub/developer_overview.html#symbols-visibility\" rel=\"nofollow\"\u003esymbols includes the CUDA architectures used for compilation\u003c/a\u003e. Therefore, a \u003ccode\u003ethrust::\u003c/code\u003e or \u003ccode\u003ecub::\u003c/code\u003e symbol may have a different ABI if:\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003ecompiled with different architectures\u003c/li\u003e\n\u003cli\u003ecompiled as a CUDA source file (\u003ccode\u003e-x cu\u003c/code\u003e) vs C++ source (\u003ccode\u003e-x cpp\u003c/code\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eSymbols in the \u003ccode\u003ecuda::\u003c/code\u003e namespace may also break ABI at any time. However, \u003ccode\u003ecuda::\u003c/code\u003e symbols embed an ABI version number that is incremented whenever an ABI break occurs. Multiple ABI versions may be supported concurrently, and therefore users have the option to revert to a prior ABI version. For more information, see \u003ca href=\"/NVIDIA/cccl/blob/main/libcudacxx/docs/releases/versioning.md\"\u003ehere\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eWho should care about ABI?\u003c/strong\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eIn general, CCCL users only need to worry about ABI issues when building or using a binary artifact (like a shared library) whose API directly or indirectly includes types provided by CCCL.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eFor example, consider if \u003ccode\u003elibA.so\u003c/code\u003e was built using CCCL version \u003ccode\u003eX\u003c/code\u003e and its public API includes a function like:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-c++ notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"void foo(cuda::std::optional\u0026lt;int\u0026gt;);\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003evoid\u003c/span\u003e \u003cspan class=\"pl-en\"\u003efoo\u003c/span\u003e(cuda::std::optional\u0026lt;\u003cspan class=\"pl-k\"\u003eint\u003c/span\u003e\u0026gt;);\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eIf another library, \u003ccode\u003elibB.so\u003c/code\u003e, is compiled using CCCL version \u003ccode\u003eY\u003c/code\u003e and uses \u003ccode\u003efoo\u003c/code\u003e from \u003ccode\u003elibA.so\u003c/code\u003e, then this can fail if there was an ABI break between version \u003ccode\u003eX\u003c/code\u003e and \u003ccode\u003eY\u003c/code\u003e.\nUnlike with API breaking changes, ABI breaks usually do not require code changes and only require recompiling everything to use the same ABI version.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eTo learn more about ABI and why it is important, see \u003ca href=\"https://wg21.link/P2028R0\" rel=\"nofollow\"\u003eWhat is ABI, and What Should C++ Do About It?\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCompatibility Guidelines\u003c/h3\u003e\u003ca id=\"user-content-compatibility-guidelines\" class=\"anchor\" aria-label=\"Permalink: Compatibility Guidelines\" href=\"#compatibility-guidelines\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eAs mentioned above, not all possible source breaking changes constitute a Breaking Change that would require incrementing CCCL's API major version number.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eUsers are encouraged to adhere to the following guidelines in order to minimize the risk of disruptions from accidentally depending on parts of CCCL that are not part of the public API:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eDo not add any declarations to, or specialize any template from, the \u003ccode\u003ethrust::\u003c/code\u003e, \u003ccode\u003ecub::\u003c/code\u003e, \u003ccode\u003env::\u003c/code\u003e, or \u003ccode\u003ecuda::\u003c/code\u003e namespaces unless an exception is noted for a specific symbol, e.g., specializing \u003ccode\u003ecuda::std::iterator_traits\u003c/code\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003cstrong\u003eRationale\u003c/strong\u003e: This would cause conflicts if a symbol or specialization is added with the same name.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eDo not take the address of any API in the \u003ccode\u003ethrust::\u003c/code\u003e, \u003ccode\u003ecub::\u003c/code\u003e, \u003ccode\u003ecuda::\u003c/code\u003e, or \u003ccode\u003env::\u003c/code\u003e namespaces.\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003cstrong\u003eRationale\u003c/strong\u003e: This would prevent adding overloads of these APIs.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eDo not forward declare any API in the \u003ccode\u003ethrust::\u003c/code\u003e, \u003ccode\u003ecub::\u003c/code\u003e, \u003ccode\u003ecuda::\u003c/code\u003e, or \u003ccode\u003env::\u003c/code\u003e namespaces.\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003cstrong\u003eRationale\u003c/strong\u003e: This would prevent adding overloads of these APIs.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eDo not directly reference any symbol prefixed with \u003ccode\u003e_\u003c/code\u003e, \u003ccode\u003e__\u003c/code\u003e, or with \u003ccode\u003edetail\u003c/code\u003e anywhere in its name including a \u003ccode\u003edetail::\u003c/code\u003e namespace or macro\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003cstrong\u003eRationale\u003c/strong\u003e: These symbols are for internal use only and may change at any time without warning.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eInclude what you use. For every CCCL symbol that you use, directly \u003ccode\u003e#include\u003c/code\u003e the header file that declares that symbol. In other words, do not rely on headers implicitly included by other headers.\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003cstrong\u003eRationale\u003c/strong\u003e: Internal includes may change at any time.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003ePortions of this section were inspired by \u003ca href=\"https://abseil.io/about/compatibility\" rel=\"nofollow\"\u003eAbseil's Compatibility Guidelines\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eDeprecation Policy\u003c/h2\u003e\u003ca id=\"user-content-deprecation-policy\" class=\"anchor\" aria-label=\"Permalink: Deprecation Policy\" href=\"#deprecation-policy\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eWe will do our best to notify users prior to making any breaking changes to the public API, ABI, or modifying the supported platforms and compilers.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eAs appropriate, deprecations will come in the form of programmatic warnings which can be disabled.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThe deprecation period will depend on the impact of the change, but will usually last at least 2 minor version releases.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eMapping to CTK Versions\u003c/h2\u003e\u003ca id=\"user-content-mapping-to-ctk-versions\" class=\"anchor\" aria-label=\"Permalink: Mapping to CTK Versions\" href=\"#mapping-to-ctk-versions\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eCCCL version\u003c/th\u003e\n\u003cth\u003eCTK version\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e3.0\u003c/td\u003e\n\u003ctd\u003e13.0\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e...\u003c/td\u003e\n\u003ctd\u003e...\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e2.8\u003c/td\u003e\n\u003ctd\u003e12.9\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e2.7\u003c/td\u003e\n\u003ctd\u003e12.8\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e2.5\u003c/td\u003e\n\u003ctd\u003e12.6\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e2.4\u003c/td\u003e\n\u003ctd\u003e12.5\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e2.3\u003c/td\u003e\n\u003ctd\u003e12.4\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cp dir=\"auto\"\u003eTest yourself: \u003ca href=\"https://cuda.godbolt.org/z/K818M4Y9f\" rel=\"nofollow\"\u003ehttps://cuda.godbolt.org/z/K818M4Y9f\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eCTKs before 12.4 shipped Thrust, CUB and libcudacxx as individual libraries.\u003c/p\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eThrust/CUB/libcudacxx version\u003c/th\u003e\n\u003cth\u003eCTK version\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e2.2\u003c/td\u003e\n\u003ctd\u003e12.3\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e2.1\u003c/td\u003e\n\u003ctd\u003e12.2\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e2.0/2.0/1.9\u003c/td\u003e\n\u003ctd\u003e12.1\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e2.0/2.0/1.9\u003c/td\u003e\n\u003ctd\u003e12.0\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCI Pipeline Overview\u003c/h2\u003e\u003ca id=\"user-content-ci-pipeline-overview\" class=\"anchor\" aria-label=\"Permalink: CI Pipeline Overview\" href=\"#ci-pipeline-overview\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eFor a detailed overview of the CI pipeline, see \u003ca href=\"/NVIDIA/cccl/blob/main/ci-overview.md\"\u003eci-overview.md\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eRelated Projects\u003c/h2\u003e\u003ca id=\"user-content-related-projects\" class=\"anchor\" aria-label=\"Permalink: Related Projects\" href=\"#related-projects\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eProjects that are related to CCCL's mission to make CUDA more delightful:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/NVIDIA/cuCollections\"\u003ecuCollections\u003c/a\u003e - GPU accelerated data structures like hash tables\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/NVIDIA/nvbench\"\u003eNVBench\u003c/a\u003e - Benchmarking library tailored for CUDA applications\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/nvidia/stdexec\"\u003estdexec\u003c/a\u003e - Reference implementation for Senders asynchronous programming model\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eProjects Using CCCL\u003c/h2\u003e\u003ca id=\"user-content-projects-using-cccl\" class=\"anchor\" aria-label=\"Permalink: Projects Using CCCL\" href=\"#projects-using-cccl\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eDoes your project use CCCL? \u003ca href=\"https://github.com/NVIDIA/cccl/edit/main/README.md\"\u003eOpen a PR to add your project to this list!\u003c/a\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/NVIDIA/AMGX\"\u003eAmgX\u003c/a\u003e - Multi-grid linear solver library\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/hpcaitech/ColossalAI\"\u003eColossalAI\u003c/a\u003e - Tools for writing distributed deep learning models\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/rapidsai/cudf\"\u003ecuDF\u003c/a\u003e - Algorithms and file readers for ETL data analytics\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/rapidsai/cugraph\"\u003ecuGraph\u003c/a\u003e - Algorithms for graph analytics\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/rapidsai/cuml\"\u003ecuML\u003c/a\u003e - Machine learning algorithms and primitives\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://cupy.dev\" rel=\"nofollow\"\u003eCuPy\u003c/a\u003e - NumPy \u0026amp; SciPy for GPU\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://developer.nvidia.com/cusolver\" rel=\"nofollow\"\u003ecuSOLVER\u003c/a\u003e - Dense and sparse linear solvers\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/rapidsai/cuspatial\"\u003ecuSpatial\u003c/a\u003e - Algorithms for geospatial operations\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/GooFit/GooFit\"\u003eGooFit\u003c/a\u003e - Library for maximum-likelihood fits\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/heavyai/heavydb\"\u003eHeavyDB\u003c/a\u003e - SQL database engine\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/glotzerlab/hoomd-blue\"\u003eHOOMD\u003c/a\u003e - Monte Carlo and molecular dynamics simulations\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/NVIDIA-Merlin/HugeCTR\"\u003eHugeCTR\u003c/a\u003e - GPU-accelerated recommender framework\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/MultithreadCorner/Hydra\"\u003eHydra\u003c/a\u003e - High-energy Physics Data Analysis\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/hypre-space/hypre\"\u003eHypre\u003c/a\u003e - Multigrid linear solvers\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/bytedance/lightseq\"\u003eLightSeq\u003c/a\u003e - Training and inference for sequence processing and generation\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/NVIDIA/matx\"\u003eMatX\u003c/a\u003e - Numerical computing library using expression templates to provide efficient, Python-like syntax\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/pytorch/pytorch\"\u003ePyTorch\u003c/a\u003e - Tensor and neural network computations\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Qiskit/qiskit-aer\"\u003eQiskit\u003c/a\u003e - High performance simulator for quantum circuits\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/lattice/quda\"\u003eQUDA\u003c/a\u003e - Lattice quantum chromodynamics (QCD) computations\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/rapidsai/raft\"\u003eRAFT\u003c/a\u003e - Algorithms and primitives for machine learning\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/tensorflow/tensorflow\"\u003eTensorFlow\u003c/a\u003e - End-to-end platform for machine learning\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/NVIDIA/TensorRT\"\u003eTensorRT\u003c/a\u003e - Deep learning inference\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/CannyLab/tsne-cuda\"\u003etsne-cuda\u003c/a\u003e - Stochastic Neighborhood Embedding library\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://gitlab.kitware.com/vtk/vtk\" rel=\"nofollow\"\u003eVisualization Toolkit (VTK)\u003c/a\u003e - Rendering and visualization library\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/dmlc/xgboost\"\u003eXGBoost\u003c/a\u003e - Gradient boosting machine learning algorithms\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/article\u003e","loaded":true,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":[{"level":1,"text":"CUDA Core Compute Libraries (CCCL)","anchor":"cuda-core-compute-libraries-cccl","htmlText":"CUDA Core Compute Libraries (CCCL)"},{"level":2,"text":"Overview","anchor":"overview","htmlText":"Overview"},{"level":2,"text":"Example","anchor":"example","htmlText":"Example"},{"level":2,"text":"Getting Started","anchor":"getting-started","htmlText":"Getting Started"},{"level":3,"text":"Users","anchor":"users","htmlText":"Users"},{"level":4,"text":"CUDA Toolkit","anchor":"cuda-toolkit","htmlText":"CUDA Toolkit"},{"level":4,"text":"GitHub","anchor":"github","htmlText":"GitHub"},{"level":5,"text":"Installation","anchor":"installation","htmlText":"Installation"},{"level":4,"text":"Conda","anchor":"conda","htmlText":"Conda"},{"level":5,"text":"CMake Integration","anchor":"cmake-integration","htmlText":"CMake Integration"},{"level":3,"text":"Contributors","anchor":"contributors","htmlText":"Contributors"},{"level":2,"text":"Platform Support","anchor":"platform-support","htmlText":"Platform Support"},{"level":3,"text":"CUDA Toolkit (CTK) Compatibility","anchor":"cuda-toolkit-ctk-compatibility","htmlText":"CUDA Toolkit (CTK) Compatibility"},{"level":3,"text":"Operating Systems","anchor":"operating-systems","htmlText":"Operating Systems"},{"level":3,"text":"Host Compilers","anchor":"host-compilers","htmlText":"Host Compilers"},{"level":3,"text":"C++ Dialects","anchor":"c-dialects","htmlText":"C++ Dialects"},{"level":3,"text":"GPU Architectures","anchor":"gpu-architectures","htmlText":"GPU Architectures"},{"level":3,"text":"Testing Strategy","anchor":"testing-strategy","htmlText":"Testing Strategy"},{"level":2,"text":"Versioning","anchor":"versioning","htmlText":"Versioning"},{"level":3,"text":"Breaking Change","anchor":"breaking-change","htmlText":"Breaking Change"},{"level":3,"text":"Application Programming Interface (API)","anchor":"application-programming-interface-api","htmlText":"Application Programming Interface (API)"},{"level":4,"text":"API Versioning","anchor":"api-versioning","htmlText":"API Versioning"},{"level":3,"text":"Application Binary Interface (ABI)","anchor":"application-binary-interface-abi","htmlText":"Application Binary Interface (ABI)"},{"level":3,"text":"Compatibility Guidelines","anchor":"compatibility-guidelines","htmlText":"Compatibility Guidelines"},{"level":2,"text":"Deprecation Policy","anchor":"deprecation-policy","htmlText":"Deprecation Policy"},{"level":2,"text":"Mapping to CTK Versions","anchor":"mapping-to-ctk-versions","htmlText":"Mapping to CTK Versions"},{"level":2,"text":"CI Pipeline Overview","anchor":"ci-pipeline-overview","htmlText":"CI Pipeline Overview"},{"level":2,"text":"Related Projects","anchor":"related-projects","htmlText":"Related Projects"},{"level":2,"text":"Projects Using CCCL","anchor":"projects-using-cccl","htmlText":"Projects Using CCCL"}],"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2FNVIDIA%2Fcccl"}},{"displayName":"CODE_OF_CONDUCT.md","repoName":"cccl","refName":"main","path":"CODE_OF_CONDUCT.md","preferredFileType":"code_of_conduct","tabName":"Code of 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d="M1.75 1A1.75 1.75 0 0 0 0 2.75v10.5C0 14.216.784 15 1.75 15h12.5A1.75 1.75 0 0 0 16 13.25v-8.5A1.75 1.75 0 0 0 14.25 3H7.5a.25.25 0 0 1-.2-.1l-.9-1.2C6.07 1.26 5.55 1 5 1H1.75Z"></path></svg><div class="overflow-hidden"><div class="react-directory-filename-cell"><div class="react-directory-truncate"><a title="c" aria-label="c, (Directory)" class="Link--primary" href="/NVIDIA/cccl/tree/main/c">c</a></div></div></div></div></td><td class="react-directory-row-name-cell-large-screen" colSpan="1"><div class="react-directory-filename-column"><svg aria-hidden="true" focusable="false" class="octicon octicon-file-directory-fill icon-directory" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="M1.75 1A1.75 1.75 0 0 0 0 2.75v10.5C0 14.216.784 15 1.75 15h12.5A1.75 1.75 0 0 0 16 13.25v-8.5A1.75 1.75 0 0 0 14.25 3H7.5a.25.25 0 0 1-.2-.1l-.9-1.2C6.07 1.26 5.55 1 5 1H1.75Z"></path></svg><div 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1.5h-8.5a.75.75 0 0 1 0-1.5Zm0 5h8.5a.75.75 0 0 1 0 1.5h-8.5a.75.75 0 0 1 0-1.5ZM2 14a1 1 0 1 1 0-2 1 1 0 0 1 0 2Zm1-6a1 1 0 1 1-2 0 1 1 0 0 1 2 0ZM2 4a1 1 0 1 1 0-2 1 1 0 0 1 0 2Z"></path></svg></button></div><div class="Box-sc-g0xbh4-0 QkQOb js-snippet-clipboard-copy-unpositioned undefined" data-hpc="true"><article class="markdown-body entry-content container-lg" itemprop="text"><p dir="auto"><a href="https://codespaces.new/NVIDIA/cccl?quickstart=1&devcontainer_path=.devcontainer%2Fdevcontainer.json" rel="nofollow"><img src="https://github.com/codespaces/badge.svg" alt="Open in GitHub Codespaces" style="max-width: 100%;"></a></p> <markdown-accessiblity-table><table> <thead> <tr> <th><a href="https://github.com/NVIDIA/cccl/blob/main/CONTRIBUTING.md">Contributor Guide</a></th> <th><a href="https://github.com/NVIDIA/cccl/blob/main/.devcontainer/README.md">Dev Containers</a></th> <th><a href="https://discord.gg/nvidiadeveloper" rel="nofollow">Discord</a></th> <th><a href="https://godbolt.org/z/x4G73af9a" rel="nofollow">Godbolt</a></th> <th><a href="https://github.com/orgs/NVIDIA/projects/6">GitHub Project</a></th> <th><a href="https://nvidia.github.io/cccl" rel="nofollow">Documentation</a></th> </tr> </thead> </table></markdown-accessiblity-table> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">CUDA Core Compute Libraries (CCCL)</h1><a id="user-content-cuda-core-compute-libraries-cccl" class="anchor" aria-label="Permalink: CUDA Core Compute Libraries (CCCL)" href="#cuda-core-compute-libraries-cccl"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Welcome to the CUDA Core Compute Libraries (CCCL) where our mission is to make CUDA more delightful.</p> <p dir="auto">This repository unifies three essential CUDA C++ libraries into a single, convenient repository:</p> <ul dir="auto"> <li><a href="/NVIDIA/cccl/blob/main/thrust">Thrust</a> (<a href="https://github.com/nvidia/thrust">former repo</a>)</li> <li><a href="/NVIDIA/cccl/blob/main/cub">CUB</a> (<a href="https://github.com/nvidia/cub">former repo</a>)</li> <li><a href="/NVIDIA/cccl/blob/main/libcudacxx">libcudacxx</a> (<a href="https://github.com/nvidia/libcudacxx">former repo</a>)</li> </ul> <p dir="auto">The goal of CCCL is to provide CUDA C++ developers with building blocks that make it easier to write safe and efficient code. Bringing these libraries together streamlines your development process and broadens your ability to leverage the power of CUDA C++. For more information about the decision to unify these projects, see the <a href="https://github.com/NVIDIA/cccl/discussions/520" data-hovercard-type="discussion" data-hovercard-url="/NVIDIA/cccl/discussions/520/hovercard">announcement here</a>.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Overview</h2><a id="user-content-overview" class="anchor" aria-label="Permalink: Overview" href="#overview"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">The concept for the CUDA Core Compute Libraries (CCCL) grew organically out of the Thrust, CUB, and libcudacxx projects that were developed independently over the years with a similar goal: to provide high-quality, high-performance, and easy-to-use C++ abstractions for CUDA developers. Naturally, there was a lot of overlap among the three projects, and it became clear the community would be better served by unifying them into a single repository.</p> <ul dir="auto"> <li> <p dir="auto"><strong>Thrust</strong> is the C++ parallel algorithms library which inspired the introduction of parallel algorithms to the C++ Standard Library. Thrust's high-level interface greatly enhances programmer productivity while enabling performance portability between GPUs and multicore CPUs via configurable backends that allow using multiple parallel programming frameworks (such as CUDA, TBB, and OpenMP).</p> </li> <li> <p dir="auto"><strong>CUB</strong> is a lower-level, CUDA-specific library designed for speed-of-light parallel algorithms across all GPU architectures. In addition to device-wide algorithms, it provides <em>cooperative algorithms</em> like block-wide reduction and warp-wide scan, providing CUDA kernel developers with building blocks to create speed-of-light, custom kernels.</p> </li> <li> <p dir="auto"><strong>libcudacxx</strong> is the CUDA C++ Standard Library. It provides an implementation of the C++ Standard Library that works in both host and device code. Additionally, it provides abstractions for CUDA-specific hardware features like synchronization primitives, cache control, atomics, and more.</p> </li> </ul> <p dir="auto">The main goal of CCCL is to fill a similar role that the Standard C++ Library fills for Standard C++: provide general-purpose, speed-of-light tools to CUDA C++ developers, allowing them to focus on solving the problems that matter. Unifying these projects is the first step towards realizing that goal.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Example</h2><a id="user-content-example" class="anchor" aria-label="Permalink: Example" href="#example"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">This is a simple example demonstrating the use of CCCL functionality from Thrust, CUB, and libcudacxx.</p> <p dir="auto">It shows how to use Thrust/CUB/libcudacxx to implement a simple parallel reduction kernel. Each thread block computes the sum of a subset of the array using <code>cub::BlockReduce</code>. The sum of each block is then reduced to a single value using an atomic add via <code>cuda::atomic_ref</code> from libcudacxx.</p> <p dir="auto">It then shows how the same reduction can be done using Thrust's <code>reduce</code> algorithm and compares the results.</p> <p dir="auto"><a href="https://godbolt.org/z/aMx4j9f4T" rel="nofollow">Try it live on Godbolt!</a></p> <div class="highlight highlight-source-c++ notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="#include <thrust/execution_policy.h> #include <thrust/device_vector.h> #include <cub/block/block_reduce.cuh> #include <cuda/atomic> #include <cuda/cmath> #include <cuda/std/span> #include <cstdio> template <int block_size> __global__ void reduce(cuda::std::span<int const> data, cuda::std::span<int> result) { using BlockReduce = cub::BlockReduce<int, block_size>; __shared__ typename BlockReduce::TempStorage temp_storage; int const index = threadIdx.x + blockIdx.x * blockDim.x; int sum = 0; if (index < data.size()) { sum += data[index]; } sum = BlockReduce(temp_storage).Sum(sum); if (threadIdx.x == 0) { cuda::atomic_ref<int, cuda::thread_scope_device> atomic_result(result.front()); atomic_result.fetch_add(sum, cuda::memory_order_relaxed); } } int main() { // Allocate and initialize input data int const N = 1000; thrust::device_vector<int> data(N); thrust::fill(data.begin(), data.end(), 1); // Allocate output data thrust::device_vector<int> kernel_result(1); // Compute the sum reduction of `data` using a custom kernel constexpr int block_size = 256; int const num_blocks = cuda::ceil_div(N, block_size); reduce<block_size><<<num_blocks, block_size>>>(cuda::std::span<int const>(thrust::raw_pointer_cast(data.data()), data.size()), cuda::std::span<int>(thrust::raw_pointer_cast(kernel_result.data()), 1)); auto const err = cudaDeviceSynchronize(); if (err != cudaSuccess) { std::cout << "Error: " << cudaGetErrorString(err) << std::endl; return -1; } int const custom_result = kernel_result[0]; // Compute the same sum reduction using Thrust int const thrust_result = thrust::reduce(thrust::device, data.begin(), data.end(), 0); // Ensure the two solutions are identical std::printf("Custom kernel sum: %d\n", custom_result); std::printf("Thrust reduce sum: %d\n", thrust_result); assert(kernel_result[0] == thrust_result); return 0; }"><pre>#<span class="pl-k">include</span> <span class="pl-s"><span class="pl-pds"><</span>thrust/execution_policy.h<span class="pl-pds">></span></span> #<span class="pl-k">include</span> <span class="pl-s"><span class="pl-pds"><</span>thrust/device_vector.h<span class="pl-pds">></span></span> #<span class="pl-k">include</span> <span class="pl-s"><span class="pl-pds"><</span>cub/block/block_reduce.cuh<span class="pl-pds">></span></span> #<span class="pl-k">include</span> <span class="pl-s"><span class="pl-pds"><</span>cuda/atomic<span class="pl-pds">></span></span> #<span class="pl-k">include</span> <span class="pl-s"><span class="pl-pds"><</span>cuda/cmath<span class="pl-pds">></span></span> #<span class="pl-k">include</span> <span class="pl-s"><span class="pl-pds"><</span>cuda/std/span<span class="pl-pds">></span></span> #<span class="pl-k">include</span> <span class="pl-s"><span class="pl-pds"><</span>cstdio<span class="pl-pds">></span></span> <span class="pl-k">template </span><<span class="pl-k">int</span> block_size> __global__ <span class="pl-k">void</span> <span class="pl-en">reduce</span>(cuda::std::span<<span class="pl-k">int</span> <span class="pl-k">const</span>> data, cuda::std::span<<span class="pl-k">int</span>> result) { <span class="pl-k">using</span> BlockReduce = cub::BlockReduce<<span class="pl-k">int</span>, block_size>; __shared__ <span class="pl-k">typename</span> BlockReduce::TempStorage temp_storage; <span class="pl-k">int</span> <span class="pl-k">const</span> <span class="pl-c1">index</span> = threadIdx.<span class="pl-smi">x</span> + blockIdx.<span class="pl-smi">x</span> * blockDim.<span class="pl-smi">x</span>; <span class="pl-k">int</span> sum = <span class="pl-c1">0</span>; <span class="pl-k">if</span> (<span class="pl-c1">index</span> < data.<span class="pl-c1">size</span>()) { sum += data[<span class="pl-c1">index</span>]; } sum = <span class="pl-c1">BlockReduce</span>(temp_storage).<span class="pl-c1">Sum</span>(sum); <span class="pl-k">if</span> (threadIdx.<span class="pl-smi">x</span> == <span class="pl-c1">0</span>) { cuda::atomic_ref<<span class="pl-k">int</span>, cuda::thread_scope_device> <span class="pl-c1">atomic_result</span>(result.<span class="pl-c1">front</span>()); atomic_result.<span class="pl-c1">fetch_add</span>(sum, cuda::memory_order_relaxed); } } <span class="pl-k">int</span> <span class="pl-en">main</span>() { <span class="pl-c"><span class="pl-c">//</span> Allocate and initialize input data</span> <span class="pl-k">int</span> <span class="pl-k">const</span> N = <span class="pl-c1">1000</span>; thrust::device_vector<<span class="pl-k">int</span>> <span class="pl-c1">data</span>(N); <span class="pl-c1">thrust::fill</span>(data.<span class="pl-c1">begin</span>(), data.<span class="pl-c1">end</span>(), <span class="pl-c1">1</span>); <span class="pl-c"><span class="pl-c">//</span> Allocate output data</span> thrust::device_vector<<span class="pl-k">int</span>> <span class="pl-c1">kernel_result</span>(<span class="pl-c1">1</span>); <span class="pl-c"><span class="pl-c">//</span> Compute the sum reduction of `data` using a custom kernel</span> <span class="pl-k">constexpr</span> <span class="pl-k">int</span> block_size = <span class="pl-c1">256</span>; <span class="pl-k">int</span> <span class="pl-k">const</span> num_blocks = <span class="pl-c1">cuda::ceil_div</span>(N, block_size); reduce<block_size><<<num_blocks, block_size>>>(cuda::std::span<<span class="pl-k">int</span> <span class="pl-k">const</span>>(<span class="pl-c1">thrust::raw_pointer_cast</span>(data.<span class="pl-c1">data</span>()), data.<span class="pl-c1">size</span>()), cuda::std::span<<span class="pl-k">int</span>>(<span class="pl-c1">thrust::raw_pointer_cast</span>(kernel_result.<span class="pl-c1">data</span>()), <span class="pl-c1">1</span>)); <span class="pl-k">auto</span> <span class="pl-k">const</span> err = <span class="pl-c1">cudaDeviceSynchronize</span>(); <span class="pl-k">if</span> (err != cudaSuccess) { std::cout << <span class="pl-s"><span class="pl-pds">"</span>Error: <span class="pl-pds">"</span></span> << <span class="pl-c1">cudaGetErrorString</span>(err) << std::endl; <span class="pl-k">return</span> -<span class="pl-c1">1</span>; } <span class="pl-k">int</span> <span class="pl-k">const</span> custom_result = kernel_result[<span class="pl-c1">0</span>]; <span class="pl-c"><span class="pl-c">//</span> Compute the same sum reduction using Thrust</span> <span class="pl-k">int</span> <span class="pl-k">const</span> thrust_result = <span class="pl-c1">thrust::reduce</span>(thrust::device, data.<span class="pl-c1">begin</span>(), data.<span class="pl-c1">end</span>(), <span class="pl-c1">0</span>); <span class="pl-c"><span class="pl-c">//</span> Ensure the two solutions are identical</span> <span class="pl-c1">std::printf</span>(<span class="pl-s"><span class="pl-pds">"</span>Custom kernel sum: %d<span class="pl-cce">\n</span><span class="pl-pds">"</span></span>, custom_result); <span class="pl-c1">std::printf</span>(<span class="pl-s"><span class="pl-pds">"</span>Thrust reduce sum: %d<span class="pl-cce">\n</span><span class="pl-pds">"</span></span>, thrust_result); <span class="pl-c1">assert</span>(kernel_result[<span class="pl-c1">0</span>] == thrust_result); <span class="pl-k">return</span> <span class="pl-c1">0</span>; }</pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Getting Started</h2><a id="user-content-getting-started" class="anchor" aria-label="Permalink: Getting Started" href="#getting-started"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Users</h3><a id="user-content-users" class="anchor" aria-label="Permalink: Users" href="#users"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Everything in CCCL is header-only. Therefore, users need only concern themselves with how they get the header files and how they incorporate them into their build system.</p> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">CUDA Toolkit</h4><a id="user-content-cuda-toolkit" class="anchor" aria-label="Permalink: CUDA Toolkit" href="#cuda-toolkit"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">The easiest way to get started using CCCL is via the <a href="https://developer.nvidia.com/cuda-toolkit" rel="nofollow">CUDA Toolkit</a> which includes the CCCL headers. When you compile with <code>nvcc</code>, it automatically adds CCCL headers to your include path so you can simply <code>#include</code> any CCCL header in your code with no additional configuration required.</p> <p dir="auto">If compiling with another compiler, you will need to update your build system's include search path to point to the CCCL headers in your CTK install (e.g., <code>/usr/local/cuda/include</code>).</p> <div class="highlight highlight-source-c++ notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="#include <thrust/device_vector.h> #include <cub/cub.cuh> #include <cuda/std/atomic>"><pre>#<span class="pl-k">include</span> <span class="pl-s"><span class="pl-pds"><</span>thrust/device_vector.h<span class="pl-pds">></span></span> #<span class="pl-k">include</span> <span class="pl-s"><span class="pl-pds"><</span>cub/cub.cuh<span class="pl-pds">></span></span> #<span class="pl-k">include</span> <span class="pl-s"><span class="pl-pds"><</span>cuda/std/atomic<span class="pl-pds">></span></span></pre></div> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">GitHub</h4><a id="user-content-github" class="anchor" aria-label="Permalink: GitHub" href="#github"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Users who want to stay on the cutting edge of CCCL development are encouraged to use CCCL from GitHub. Using a newer version of CCCL with an older version of the CUDA Toolkit is supported, but not the other way around. For complete information on compatibility between CCCL and the CUDA Toolkit, see <a href="#platform-support">our platform support</a>.</p> <p dir="auto">Everything in CCCL is header-only, so cloning and including it in a simple project is as easy as the following:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="git clone https://github.com/NVIDIA/cccl.git nvcc -Icccl/thrust -Icccl/libcudacxx/include -Icccl/cub main.cu -o main"><pre>git clone https://github.com/NVIDIA/cccl.git nvcc -Icccl/thrust -Icccl/libcudacxx/include -Icccl/cub main.cu -o main</pre></div> <blockquote> <p dir="auto"><strong>Note</strong> Use <code>-I</code> and not <code>-isystem</code> to avoid collisions with the CCCL headers implicitly included by <code>nvcc</code> from the CUDA Toolkit. All CCCL headers use <code>#pragma system_header</code> to ensure warnings will still be silenced as if using <code>-isystem</code>, see <a class="issue-link js-issue-link" data-error-text="Failed to load title" data-id="1930979977" data-permission-text="Title is private" data-url="https://github.com/NVIDIA/cccl/issues/527" data-hovercard-type="issue" data-hovercard-url="/NVIDIA/cccl/issues/527/hovercard" href="https://github.com/NVIDIA/cccl/issues/527">#527</a> for more information.</p> </blockquote> <div class="markdown-heading" dir="auto"><h5 tabindex="-1" class="heading-element" dir="auto">Installation</h5><a id="user-content-installation" class="anchor" aria-label="Permalink: Installation" href="#installation"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">A minimal build that only generates installation rules can be configured using the <code>install</code> CMake preset:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="git clone https://github.com/NVIDIA/cccl.git cd cccl cmake --preset install -DCMAKE_INSTALL_PREFIX=/usr/local/ cd build/install ninja install"><pre>git clone https://github.com/NVIDIA/cccl.git <span class="pl-c1">cd</span> cccl cmake --preset install -DCMAKE_INSTALL_PREFIX=/usr/local/ <span class="pl-c1">cd</span> build/install ninja install</pre></div> <p dir="auto">To include experimental libraries in the installation, use the <code>install-unstable</code> preset and build directory.</p> <p dir="auto">To install <strong>only</strong> the experimental libraries, use the <code>install-unstable-only</code> preset and build directory.</p> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Conda</h4><a id="user-content-conda" class="anchor" aria-label="Permalink: Conda" href="#conda"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">CCCL also provides conda packages of each release via the <code>conda-forge</code> channel:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="conda config --add channels conda-forge conda install cccl"><pre>conda config --add channels conda-forge conda install cccl</pre></div> <p dir="auto">This will install the latest CCCL to the conda environment's <code>$CONDA_PREFIX/include/</code> and <code>$CONDA_PREFIX/lib/cmake/</code> directories. It is discoverable by CMake via <code>find_package(CCCL)</code> and can be used by any compilers in the conda environment. For more information, see <a href="https://conda-forge.org/docs/user/introduction/" rel="nofollow">this introduction to conda-forge</a>.</p> <p dir="auto">If you want to use the same CCCL version that shipped with a particular CUDA Toolkit, e.g. CUDA 12.4, you can install CCCL with:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="conda config --add channels conda-forge conda install cuda-cccl cuda-version=12.4"><pre>conda config --add channels conda-forge conda install cuda-cccl cuda-version=12.4</pre></div> <p dir="auto">The <code>cuda-cccl</code> metapackage installs the <code>cccl</code> version that shipped with the CUDA Toolkit corresponding to <code>cuda-version</code>. If you wish to update to the latest <code>cccl</code> after installing <code>cuda-cccl</code>, uninstall <code>cuda-cccl</code> before updating <code>cccl</code>:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="conda uninstall cuda-cccl conda install -c conda-forge cccl"><pre>conda uninstall cuda-cccl conda install -c conda-forge cccl</pre></div> <blockquote> <p dir="auto"><strong>Note</strong> There are also conda packages with names like <code>cuda-cccl_linux-64</code>. Those packages contain the CCCL versions shipped as part of the CUDA Toolkit, but are designed for internal use by the CUDA Toolkit. Install <code>cccl</code> or <code>cuda-cccl</code> instead, for compatibility with conda compilers. For more information, see the <a href="https://github.com/conda-forge/cccl-feedstock/blob/main/recipe/meta.yaml">cccl conda-forge recipe</a>.</p> </blockquote> <div class="markdown-heading" dir="auto"><h5 tabindex="-1" class="heading-element" dir="auto">CMake Integration</h5><a id="user-content-cmake-integration" class="anchor" aria-label="Permalink: CMake Integration" href="#cmake-integration"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">CCCL uses <a href="https://cmake.org/" rel="nofollow">CMake</a> for all build and installation infrastructure, including tests as well as targets to link against in other CMake projects. Therefore, CMake is the recommended way to integrate CCCL into another project.</p> <p dir="auto">For a complete example of how to do this using CMake Package Manager see <a href="/NVIDIA/cccl/blob/main/examples/basic">our basic example project</a>.</p> <p dir="auto">Other build systems should work, but only CMake is tested. Contributions to simplify integrating CCCL into other build systems are welcome.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Contributors</h3><a id="user-content-contributors" class="anchor" aria-label="Permalink: Contributors" href="#contributors"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Interested in contributing to making CCCL better? Check out our <a href="/NVIDIA/cccl/blob/main/CONTRIBUTING.md">Contributing Guide</a> for a comprehensive overview of everything you need to know to set up your development environment, make changes, run tests, and submit a PR.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Platform Support</h2><a id="user-content-platform-support" class="anchor" aria-label="Permalink: Platform Support" href="#platform-support"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><strong>Objective:</strong> This section describes where users can expect CCCL to compile and run successfully.</p> <p dir="auto">In general, CCCL should work everywhere the CUDA Toolkit is supported, however, the devil is in the details. The sections below describe the details of support and testing for different versions of the CUDA Toolkit, host compilers, and C++ dialects.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">CUDA Toolkit (CTK) Compatibility</h3><a id="user-content-cuda-toolkit-ctk-compatibility" class="anchor" aria-label="Permalink: CUDA Toolkit (CTK) Compatibility" href="#cuda-toolkit-ctk-compatibility"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><strong>Summary:</strong></p> <ul dir="auto"> <li>The latest version of CCCL is backward compatible with the current and preceding CTK major version series</li> <li>CCCL is never forward compatible with any version of the CTK. Always use the same or newer than what is included with your CTK.</li> <li>Minor version CCCL upgrades won't break existing code, but new features may not support all CTK versions</li> </ul> <p dir="auto">CCCL users are encouraged to capitalize on the latest enhancements and <a href="https://www.youtube.com/watch?v=tISy7EJQPzI" rel="nofollow">"live at head"</a> by always using the newest version of CCCL. For a seamless experience, you can upgrade CCCL independently of the entire CUDA Toolkit. This is possible because CCCL maintains backward compatibility with the latest patch release of every minor CTK release from both the current and previous major version series. In some exceptional cases, the minimum supported minor version of the CUDA Toolkit release may need to be newer than the oldest release within its major version series.</p> <p dir="auto">When a new major CTK is released, we drop support for the oldest supported major version.</p> <markdown-accessiblity-table><table> <thead> <tr> <th>CCCL Version</th> <th>Supports CUDA Toolkit Version</th> </tr> </thead> <tbody> <tr> <td>2.x</td> <td>11.1 - 11.8, 12.x (only latest patch releases)</td> </tr> <tr> <td>3.x</td> <td>12.x, 13.x (only latest patch releases)</td> </tr> </tbody> </table></markdown-accessiblity-table> <p dir="auto"><a href="#compatibility-guidelines">Well-behaved code</a> using the latest CCCL should compile and run successfully with any supported CTK version. Exceptions may occur for new features that depend on new CTK features, so those features would not work on older versions of the CTK.</p> <p dir="auto">Users can integrate a newer version of CCCL into an older CTK, but not the other way around. This means an older version of CCCL is not compatible with a newer CTK. In other words, <strong>CCCL is never forward compatible with the CUDA Toolkit.</strong></p> <p dir="auto">The table below summarizes compatibility of the CTK and CCCL:</p> <markdown-accessiblity-table><table> <thead> <tr> <th align="center">CTK Version</th> <th align="center">Included CCCL Version</th> <th align="center">Desired CCCL</th> <th align="center">Supported?</th> <th align="center">Notes</th> </tr> </thead> <tbody> <tr> <td align="center">CTK <code>X.Y</code></td> <td align="center">CCCL <code>MAJOR.MINOR</code></td> <td align="center">CCCL <code>MAJOR.MINOR+n</code></td> <td align="center">✅</td> <td align="center">Some new features might not work</td> </tr> <tr> <td align="center">CTK <code>X.Y</code></td> <td align="center">CCCL <code>MAJOR.MINOR</code></td> <td align="center">CCCL <code>MAJOR+1.MINOR</code></td> <td align="center">✅</td> <td align="center">Possible breaks; some new features might not be available</td> </tr> <tr> <td align="center">CTK <code>X.Y</code></td> <td align="center">CCCL <code>MAJOR.MINOR</code></td> <td align="center">CCCL <code>MAJOR+2.MINOR</code></td> <td align="center">❌</td> <td align="center">CCCL supports only two CTK major versions</td> </tr> <tr> <td align="center">CTK <code>X.Y</code></td> <td align="center">CCCL <code>MAJOR.MINOR</code></td> <td align="center">CCCL <code>MAJOR.MINOR-n</code></td> <td align="center">❌</td> <td align="center">CCCL isn't forward compatible</td> </tr> <tr> <td align="center">CTK <code>X.Y</code></td> <td align="center">CCCL <code>MAJOR.MINOR</code></td> <td align="center">CCCL <code>MAJOR-n.MINOR</code></td> <td align="center">❌</td> <td align="center">CCCL isn't forward compatible</td> </tr> </tbody> </table></markdown-accessiblity-table> <p dir="auto">For more information on CCCL versioning, API/ABI compatibility, and breaking changes see the <a href="#versioning">Versioning</a> section below.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Operating Systems</h3><a id="user-content-operating-systems" class="anchor" aria-label="Permalink: Operating Systems" href="#operating-systems"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Unless otherwise specified, CCCL supports all the same operating systems as the CUDA Toolkit, which are documented here:</p> <ul dir="auto"> <li><a href="https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#system-requirements" rel="nofollow">Linux</a></li> <li><a href="https://docs.nvidia.com/cuda/cuda-installation-guide-microsoft-windows/index.html#system-requirements" rel="nofollow">Windows</a></li> </ul> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Host Compilers</h3><a id="user-content-host-compilers" class="anchor" aria-label="Permalink: Host Compilers" href="#host-compilers"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Unless otherwise specified, CCCL supports the same host compilers as the latest CUDA Toolkit, which are documented here:</p> <ul dir="auto"> <li><a href="https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#host-compiler-support-policy" rel="nofollow">Linux</a></li> <li><a href="https://docs.nvidia.com/cuda/cuda-installation-guide-microsoft-windows/index.html#system-requirements" rel="nofollow">Windows</a></li> </ul> <p dir="auto">For GCC on Linux, at least 7.x is required.</p> <p dir="auto">When using older CUDA Toolkits, we also only support the host compilers of the latest CUDA Toolkit, but at least the most recent host compiler of any supported older CUDA Toolkit.</p> <p dir="auto">We may retain support of additional compilers and will accept corresponding patches from the community with reasonable fixes. But we will not invest significant time in triaging or fixing issues for older compilers.</p> <p dir="auto">In the spirit of "You only support what you test", see our <a href="https://github.com/NVIDIA/cccl/blob/main/ci-overview.md">CI Overview</a> for more information on exactly what we test.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">C++ Dialects</h3><a id="user-content-c-dialects" class="anchor" aria-label="Permalink: C++ Dialects" href="#c-dialects"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>C++17</li> <li>C++20</li> </ul> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">GPU Architectures</h3><a id="user-content-gpu-architectures" class="anchor" aria-label="Permalink: GPU Architectures" href="#gpu-architectures"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Unless otherwise specified, CCCL supports all the same GPU architectures/Compute Capabilities as the CUDA Toolkit, which are documented here: <a href="https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#compute-capability" rel="nofollow">https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#compute-capability</a></p> <p dir="auto">Note that some features may only support certain architectures/Compute Capabilities.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Testing Strategy</h3><a id="user-content-testing-strategy" class="anchor" aria-label="Permalink: Testing Strategy" href="#testing-strategy"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">CCCL's testing strategy strikes a balance between testing as many configurations as possible and maintaining reasonable CI times.</p> <p dir="auto">For CUDA Toolkit versions, testing is done against both the oldest and the newest supported versions. For instance, if the latest version of the CUDA Toolkit is 12.6, tests are conducted against 11.1 and 12.6. For each CUDA version, builds are completed against all supported host compilers with all supported C++ dialects.</p> <p dir="auto">The testing strategy and matrix are constantly evolving. The matrix defined in the <a href="/NVIDIA/cccl/blob/main/ci/matrix.yaml"><code>ci/matrix.yaml</code></a> file is the definitive source of truth. For more information about our CI pipeline, see <a href="/NVIDIA/cccl/blob/main/ci-overview.md">here</a>.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Versioning</h2><a id="user-content-versioning" class="anchor" aria-label="Permalink: Versioning" href="#versioning"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><strong>Objective:</strong> This section describes how CCCL is versioned, API/ABI stability guarantees, and compatibility guidelines to minimize upgrade headaches.</p> <p dir="auto"><strong>Summary</strong></p> <ul dir="auto"> <li>The entirety of CCCL's API shares a common semantic version across all components</li> <li>Only the most recently released version is supported and fixes are not backported to prior releases</li> <li>API breaking changes and incrementing CCCL's major version will only coincide with a new major version release of the CUDA Toolkit</li> <li>Not all source breaking changes are considered breaking changes of the public API that warrant bumping the major version number</li> <li>Do not rely on ABI stability of entities in the <code>cub::</code> or <code>thrust::</code> namespaces</li> <li>ABI breaking changes for symbols in the <code>cuda::</code> namespace may happen at any time, but will be reflected by incrementing the ABI version which is embedded in an inline namespace for all <code>cuda::</code> symbols. Multiple ABI versions may be supported concurrently.</li> </ul> <p dir="auto"><strong>Note:</strong> Prior to merging Thrust, CUB, and libcudacxx into this repository, each library was independently versioned according to semantic versioning. Starting with the 2.1 release, all three libraries synchronized their release versions in their separate repositories. Moving forward, CCCL will continue to be released under a single <a href="https://semver.org/" rel="nofollow">semantic version</a>, with 2.2.0 being the first release from the <a href="/NVIDIA/cccl/blob/main/www.github.com/nvidia/cccl">nvidia/cccl</a> repository.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Breaking Change</h3><a id="user-content-breaking-change" class="anchor" aria-label="Permalink: Breaking Change" href="#breaking-change"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">A Breaking Change is a change to <strong>explicitly supported</strong> functionality between released versions that would require a user to do work in order to upgrade to the newer version.</p> <p dir="auto">In the limit, <a href="https://www.hyrumslaw.com/" rel="nofollow"><em>any</em> change</a> has the potential to break someone somewhere. As a result, not all possible source breaking changes are considered Breaking Changes to the public API that warrant bumping the major semantic version.</p> <p dir="auto">The sections below describe the details of breaking changes to CCCL's API and ABI.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Application Programming Interface (API)</h3><a id="user-content-application-programming-interface-api" class="anchor" aria-label="Permalink: Application Programming Interface (API)" href="#application-programming-interface-api"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">CCCL's public API is the entirety of the functionality <em>intentionally</em> exposed to provide the utility of the library.</p> <p dir="auto">In other words, CCCL's public API goes beyond just function signatures and includes (but is not limited to):</p> <ul dir="auto"> <li>The location and names of headers intended for direct inclusion in user code</li> <li>The namespaces intended for direct use in user code</li> <li>The declarations and/or definitions of functions, classes, and variables located in headers and intended for direct use in user code</li> <li>The semantics of functions, classes, and variables intended for direct use in user code</li> </ul> <p dir="auto">Moreover, CCCL's public API does <strong>not</strong> include any of the following:</p> <ul dir="auto"> <li>Any symbol prefixed with <code>_</code> or <code>__</code></li> <li>Any symbol whose name contains <code>detail</code> including the <code>detail::</code> namespace or a macro</li> <li>Any header file contained in a <code>detail/</code> directory or sub-directory thereof</li> <li>The header files implicitly included by any header part of the public API</li> </ul> <p dir="auto">In general, the goal is to avoid breaking anything in the public API. Such changes are made only if they offer users better performance, easier-to-understand APIs, and/or more consistent APIs.</p> <p dir="auto">Any breaking change to the public API will require bumping CCCL's major version number. In keeping with <a href="https://docs.nvidia.com/deploy/cuda-compatibility/#minor-version-compatibility" rel="nofollow">CUDA Minor Version Compatibility</a>, API breaking changes and CCCL major version bumps will only occur coinciding with a new major version release of the CUDA Toolkit.</p> <p dir="auto">Anything not part of the public API may change at any time without warning.</p> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">API Versioning</h4><a id="user-content-api-versioning" class="anchor" aria-label="Permalink: API Versioning" href="#api-versioning"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">The public API of all CCCL's components share a unified semantic version of <code>MAJOR.MINOR.PATCH</code>.</p> <p dir="auto">Only the most recently released version is supported. As a rule, features and bug fixes are not backported to previously released version or branches.</p> <p dir="auto">The preferred method for querying the version is to use <code>CCCL_[MAJOR/MINOR/PATCH_]VERSION</code> as described below. For backwards compatibility, the Thrust/CUB/libcudacxxx version definitions are available and will always be consistent with <code>CCCL_VERSION</code>. Note that Thrust/CUB use a <code>MMMmmmpp</code> scheme whereas the CCCL and libcudacxx use <code>MMMmmmppp</code>.</p> <markdown-accessiblity-table><table> <thead> <tr> <th></th> <th>CCCL</th> <th>libcudacxx</th> <th>Thrust</th> <th>CUB</th> </tr> </thead> <tbody> <tr> <td>Header</td> <td><code><cuda/version></code></td> <td><code><cuda/std/version></code></td> <td><code><thrust/version.h></code></td> <td><code><cub/version.h></code></td> </tr> <tr> <td>Major Version</td> <td><code>CCCL_MAJOR_VERSION</code></td> <td><code>_LIBCUDACXX_CUDA_API_VERSION_MAJOR</code></td> <td><code>THRUST_MAJOR_VERSION</code></td> <td><code>CUB_MAJOR_VERSION</code></td> </tr> <tr> <td>Minor Version</td> <td><code>CCCL_MINOR_VERSION</code></td> <td><code>_LIBCUDACXX_CUDA_API_VERSION_MINOR</code></td> <td><code>THRUST_MINOR_VERSION</code></td> <td><code>CUB_MINOR_VERSION</code></td> </tr> <tr> <td>Patch/Subminor Version</td> <td><code>CCCL_PATCH_VERSION</code></td> <td><code>_LIBCUDACXX_CUDA_API_VERSION_PATCH</code></td> <td><code>THRUST_SUBMINOR_VERSION</code></td> <td><code>CUB_SUBMINOR_VERSION</code></td> </tr> <tr> <td>Concatenated Version</td> <td><code>CCCL_VERSION (MMMmmmppp)</code></td> <td><code>_LIBCUDACXX_CUDA_API_VERSION (MMMmmmppp)</code></td> <td><code>THRUST_VERSION (MMMmmmpp)</code></td> <td><code>CUB_VERSION (MMMmmmpp)</code></td> </tr> </tbody> </table></markdown-accessiblity-table> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Application Binary Interface (ABI)</h3><a id="user-content-application-binary-interface-abi" class="anchor" aria-label="Permalink: Application Binary Interface (ABI)" href="#application-binary-interface-abi"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">The Application Binary Interface (ABI) is a set of rules for:</p> <ul dir="auto"> <li>How a library's components are represented in machine code</li> <li>How those components interact across different translation units</li> </ul> <p dir="auto">A library's ABI includes, but is not limited to:</p> <ul dir="auto"> <li>The mangled names of functions and types</li> <li>The size and alignment of objects and types</li> <li>The semantics of the bytes in the binary representation of an object</li> </ul> <p dir="auto">An <strong>ABI Breaking Change</strong> is any change that results in a change to the ABI of a function or type in the public API. For example, adding a new data member to a struct is an ABI Breaking Change as it changes the size of the type.</p> <p dir="auto">In CCCL, the guarantees about ABI are as follows:</p> <ul dir="auto"> <li>Symbols in the <code>thrust::</code> and <code>cub::</code> namespaces may break ABI at any time without warning.</li> <li>The ABI of <code>thrust::</code> and <code>cub::</code> <a href="https://nvidia.github.io/cccl/cub/developer_overview.html#symbols-visibility" rel="nofollow">symbols includes the CUDA architectures used for compilation</a>. Therefore, a <code>thrust::</code> or <code>cub::</code> symbol may have a different ABI if: <ul dir="auto"> <li>compiled with different architectures</li> <li>compiled as a CUDA source file (<code>-x cu</code>) vs C++ source (<code>-x cpp</code>)</li> </ul> </li> <li>Symbols in the <code>cuda::</code> namespace may also break ABI at any time. However, <code>cuda::</code> symbols embed an ABI version number that is incremented whenever an ABI break occurs. Multiple ABI versions may be supported concurrently, and therefore users have the option to revert to a prior ABI version. For more information, see <a href="/NVIDIA/cccl/blob/main/libcudacxx/docs/releases/versioning.md">here</a>.</li> </ul> <p dir="auto"><strong>Who should care about ABI?</strong></p> <p dir="auto">In general, CCCL users only need to worry about ABI issues when building or using a binary artifact (like a shared library) whose API directly or indirectly includes types provided by CCCL.</p> <p dir="auto">For example, consider if <code>libA.so</code> was built using CCCL version <code>X</code> and its public API includes a function like:</p> <div class="highlight highlight-source-c++ notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="void foo(cuda::std::optional<int>);"><pre><span class="pl-k">void</span> <span class="pl-en">foo</span>(cuda::std::optional<<span class="pl-k">int</span>>);</pre></div> <p dir="auto">If another library, <code>libB.so</code>, is compiled using CCCL version <code>Y</code> and uses <code>foo</code> from <code>libA.so</code>, then this can fail if there was an ABI break between version <code>X</code> and <code>Y</code>. Unlike with API breaking changes, ABI breaks usually do not require code changes and only require recompiling everything to use the same ABI version.</p> <p dir="auto">To learn more about ABI and why it is important, see <a href="https://wg21.link/P2028R0" rel="nofollow">What is ABI, and What Should C++ Do About It?</a>.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Compatibility Guidelines</h3><a id="user-content-compatibility-guidelines" class="anchor" aria-label="Permalink: Compatibility Guidelines" href="#compatibility-guidelines"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">As mentioned above, not all possible source breaking changes constitute a Breaking Change that would require incrementing CCCL's API major version number.</p> <p dir="auto">Users are encouraged to adhere to the following guidelines in order to minimize the risk of disruptions from accidentally depending on parts of CCCL that are not part of the public API:</p> <ul dir="auto"> <li>Do not add any declarations to, or specialize any template from, the <code>thrust::</code>, <code>cub::</code>, <code>nv::</code>, or <code>cuda::</code> namespaces unless an exception is noted for a specific symbol, e.g., specializing <code>cuda::std::iterator_traits</code> <ul dir="auto"> <li><strong>Rationale</strong>: This would cause conflicts if a symbol or specialization is added with the same name.</li> </ul> </li> <li>Do not take the address of any API in the <code>thrust::</code>, <code>cub::</code>, <code>cuda::</code>, or <code>nv::</code> namespaces. <ul dir="auto"> <li><strong>Rationale</strong>: This would prevent adding overloads of these APIs.</li> </ul> </li> <li>Do not forward declare any API in the <code>thrust::</code>, <code>cub::</code>, <code>cuda::</code>, or <code>nv::</code> namespaces. <ul dir="auto"> <li><strong>Rationale</strong>: This would prevent adding overloads of these APIs.</li> </ul> </li> <li>Do not directly reference any symbol prefixed with <code>_</code>, <code>__</code>, or with <code>detail</code> anywhere in its name including a <code>detail::</code> namespace or macro <ul dir="auto"> <li><strong>Rationale</strong>: These symbols are for internal use only and may change at any time without warning.</li> </ul> </li> <li>Include what you use. For every CCCL symbol that you use, directly <code>#include</code> the header file that declares that symbol. In other words, do not rely on headers implicitly included by other headers. <ul dir="auto"> <li><strong>Rationale</strong>: Internal includes may change at any time.</li> </ul> </li> </ul> <p dir="auto">Portions of this section were inspired by <a href="https://abseil.io/about/compatibility" rel="nofollow">Abseil's Compatibility Guidelines</a>.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Deprecation Policy</h2><a id="user-content-deprecation-policy" class="anchor" aria-label="Permalink: Deprecation Policy" href="#deprecation-policy"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">We will do our best to notify users prior to making any breaking changes to the public API, ABI, or modifying the supported platforms and compilers.</p> <p dir="auto">As appropriate, deprecations will come in the form of programmatic warnings which can be disabled.</p> <p dir="auto">The deprecation period will depend on the impact of the change, but will usually last at least 2 minor version releases.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Mapping to CTK Versions</h2><a id="user-content-mapping-to-ctk-versions" class="anchor" aria-label="Permalink: Mapping to CTK Versions" href="#mapping-to-ctk-versions"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <markdown-accessiblity-table><table> <thead> <tr> <th>CCCL version</th> <th>CTK version</th> </tr> </thead> <tbody> <tr> <td>3.0</td> <td>13.0</td> </tr> <tr> <td>...</td> <td>...</td> </tr> <tr> <td>2.8</td> <td>12.9</td> </tr> <tr> <td>2.7</td> <td>12.8</td> </tr> <tr> <td>2.5</td> <td>12.6</td> </tr> <tr> <td>2.4</td> <td>12.5</td> </tr> <tr> <td>2.3</td> <td>12.4</td> </tr> </tbody> </table></markdown-accessiblity-table> <p dir="auto">Test yourself: <a href="https://cuda.godbolt.org/z/K818M4Y9f" rel="nofollow">https://cuda.godbolt.org/z/K818M4Y9f</a></p> <p dir="auto">CTKs before 12.4 shipped Thrust, CUB and libcudacxx as individual libraries.</p> <markdown-accessiblity-table><table> <thead> <tr> <th>Thrust/CUB/libcudacxx version</th> <th>CTK version</th> </tr> </thead> <tbody> <tr> <td>2.2</td> <td>12.3</td> </tr> <tr> <td>2.1</td> <td>12.2</td> </tr> <tr> <td>2.0/2.0/1.9</td> <td>12.1</td> </tr> <tr> <td>2.0/2.0/1.9</td> <td>12.0</td> </tr> </tbody> </table></markdown-accessiblity-table> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">CI Pipeline Overview</h2><a id="user-content-ci-pipeline-overview" class="anchor" aria-label="Permalink: CI Pipeline Overview" href="#ci-pipeline-overview"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">For a detailed overview of the CI pipeline, see <a href="/NVIDIA/cccl/blob/main/ci-overview.md">ci-overview.md</a>.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Related Projects</h2><a id="user-content-related-projects" class="anchor" aria-label="Permalink: Related Projects" href="#related-projects"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Projects that are related to CCCL's mission to make CUDA more delightful:</p> <ul dir="auto"> <li><a href="https://github.com/NVIDIA/cuCollections">cuCollections</a> - GPU accelerated data structures like hash tables</li> <li><a href="https://github.com/NVIDIA/nvbench">NVBench</a> - Benchmarking library tailored for CUDA applications</li> <li><a href="https://github.com/nvidia/stdexec">stdexec</a> - Reference implementation for Senders asynchronous programming model</li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Projects Using CCCL</h2><a id="user-content-projects-using-cccl" class="anchor" aria-label="Permalink: Projects Using CCCL" href="#projects-using-cccl"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Does your project use CCCL? <a href="https://github.com/NVIDIA/cccl/edit/main/README.md">Open a PR to add your project to this list!</a></p> <ul dir="auto"> <li><a href="https://github.com/NVIDIA/AMGX">AmgX</a> - Multi-grid linear solver library</li> <li><a href="https://github.com/hpcaitech/ColossalAI">ColossalAI</a> - Tools for writing distributed deep learning models</li> <li><a href="https://github.com/rapidsai/cudf">cuDF</a> - Algorithms and file readers for ETL data analytics</li> <li><a href="https://github.com/rapidsai/cugraph">cuGraph</a> - Algorithms for graph analytics</li> <li><a href="https://github.com/rapidsai/cuml">cuML</a> - Machine learning algorithms and primitives</li> <li><a href="https://cupy.dev" rel="nofollow">CuPy</a> - NumPy & SciPy for GPU</li> <li><a href="https://developer.nvidia.com/cusolver" rel="nofollow">cuSOLVER</a> - Dense and sparse linear solvers</li> <li><a href="https://github.com/rapidsai/cuspatial">cuSpatial</a> - Algorithms for geospatial operations</li> <li><a href="https://github.com/GooFit/GooFit">GooFit</a> - Library for maximum-likelihood fits</li> <li><a href="https://github.com/heavyai/heavydb">HeavyDB</a> - SQL database engine</li> <li><a href="https://github.com/glotzerlab/hoomd-blue">HOOMD</a> - Monte Carlo and molecular dynamics simulations</li> <li><a href="https://github.com/NVIDIA-Merlin/HugeCTR">HugeCTR</a> - GPU-accelerated recommender framework</li> <li><a href="https://github.com/MultithreadCorner/Hydra">Hydra</a> - High-energy Physics Data Analysis</li> <li><a href="https://github.com/hypre-space/hypre">Hypre</a> - Multigrid linear solvers</li> <li><a href="https://github.com/bytedance/lightseq">LightSeq</a> - Training and inference for sequence processing and generation</li> <li><a href="https://github.com/NVIDIA/matx">MatX</a> - Numerical computing library using expression templates to provide efficient, Python-like syntax</li> <li><a href="https://github.com/pytorch/pytorch">PyTorch</a> - Tensor and neural network computations</li> <li><a href="https://github.com/Qiskit/qiskit-aer">Qiskit</a> - High performance simulator for quantum circuits</li> <li><a href="https://github.com/lattice/quda">QUDA</a> - Lattice quantum chromodynamics (QCD) computations</li> <li><a href="https://github.com/rapidsai/raft">RAFT</a> - Algorithms and primitives for machine learning</li> <li><a href="https://github.com/tensorflow/tensorflow">TensorFlow</a> - End-to-end platform for machine learning</li> <li><a href="https://github.com/NVIDIA/TensorRT">TensorRT</a> - Deep learning inference</li> <li><a href="https://github.com/CannyLab/tsne-cuda">tsne-cuda</a> - Stochastic Neighborhood Embedding library</li> <li><a href="https://gitlab.kitware.com/vtk/vtk" rel="nofollow">Visualization Toolkit (VTK)</a> - Rendering and visualization library</li> <li><a href="https://github.com/dmlc/xgboost">XGBoost</a> - Gradient boosting machine learning algorithms</li> </ul> </article></div></div></div></div></div> <!-- --> <!-- --> <script type="application/json" id="__PRIMER_DATA_:R0:__">{"resolvedServerColorMode":"day"}</script></div> </react-partial> <input type="hidden" data-csrf="true" value="qzm4Ex6QYGhhB4uiQ1IqhxARP4rGDTmu2hU5+L1//ILulU+lQAT7BkA3rgAYzMhgWgIAJQlEIqbloECECnKi+w==" /> </div> <div data-view-component="true" class="Layout-sidebar"> <div class="BorderGrid about-margin" data-pjax> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <div class="hide-sm hide-md"> <h2 class="mb-3 h4">About</h2> <p class="f4 my-3"> CUDA Core Compute Libraries </p> <div class="my-3 d-flex flex-items-center"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-link flex-shrink-0 mr-2"> <path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 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12.347l-3.766 1.98a.75.75 0 0 1-1.088-.79l.72-4.194L.818 6.374a.75.75 0 0 1 .416-1.28l4.21-.611L7.327.668A.75.75 0 0 1 8 .25Zm0 2.445L6.615 5.5a.75.75 0 0 1-.564.41l-3.097.45 2.24 2.184a.75.75 0 0 1 .216.664l-.528 3.084 2.769-1.456a.75.75 0 0 1 .698 0l2.77 1.456-.53-3.084a.75.75 0 0 1 .216-.664l2.24-2.183-3.096-.45a.75.75 0 0 1-.564-.41L8 2.694Z"></path> </svg> <strong>1.6k</strong> stars</a> </div> <h3 class="sr-only">Watchers</h3> <div class="mt-2"> <a href="/NVIDIA/cccl/watchers" data-view-component="true" class="Link Link--muted"><svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-eye mr-2"> <path d="M8 2c1.981 0 3.671.992 4.933 2.078 1.27 1.091 2.187 2.345 2.637 3.023a1.62 1.62 0 0 1 0 1.798c-.45.678-1.367 1.932-2.637 3.023C11.67 13.008 9.981 14 8 14c-1.981 0-3.671-.992-4.933-2.078C1.797 10.83.88 9.576.43 8.898a1.62 1.62 0 0 1 0-1.798c.45-.677 1.367-1.931 2.637-3.022C4.33 2.992 6.019 2 8 2ZM1.679 7.932a.12.12 0 0 0 0 .136c.411.622 1.241 1.75 2.366 2.717C5.176 11.758 6.527 12.5 8 12.5c1.473 0 2.825-.742 3.955-1.715 1.124-.967 1.954-2.096 2.366-2.717a.12.12 0 0 0 0-.136c-.412-.621-1.242-1.75-2.366-2.717C10.824 4.242 9.473 3.5 8 3.5c-1.473 0-2.825.742-3.955 1.715-1.124.967-1.954 2.096-2.366 2.717ZM8 10a2 2 0 1 1-.001-3.999A2 2 0 0 1 8 10Z"></path> </svg> <strong>34</strong> watching</a> </div> <h3 class="sr-only">Forks</h3> <div class="mt-2"> <a href="/NVIDIA/cccl/forks" data-view-component="true" class="Link Link--muted"><svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-repo-forked mr-2"> <path d="M5 5.372v.878c0 .414.336.75.75.75h4.5a.75.75 0 0 0 .75-.75v-.878a2.25 2.25 0 1 1 1.5 0v.878a2.25 2.25 0 0 1-2.25 2.25h-1.5v2.128a2.251 2.251 0 1 1-1.5 0V8.5h-1.5A2.25 2.25 0 0 1 3.5 6.25v-.878a2.25 2.25 0 1 1 1.5 0ZM5 3.25a.75.75 0 1 0-1.5 0 .75.75 0 0 0 1.5 0Zm6.75.75a.75.75 0 1 0 0-1.5.75.75 0 0 0 0 1.5Zm-3 8.75a.75.75 0 1 0-1.5 0 .75.75 0 0 0 1.5 0Z"></path> </svg> <strong>205</strong> forks</a> </div> <div class="mt-2"> <a class="Link--muted" href="/contact/report-content?content_url=https%3A%2F%2Fgithub.com%2FNVIDIA%2Fcccl&report=NVIDIA+%28user%29"> Report repository </a> </div> </div> </div> </div> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <h2 class="h4 mb-3" data-pjax="#repo-content-pjax-container" data-turbo-frame="repo-content-turbo-frame"> <a href="/NVIDIA/cccl/releases" data-view-component="true" class="Link--primary no-underline Link">Releases <span title="12" data-view-component="true" class="Counter">12</span></a></h2> <a class="Link--primary d-flex no-underline" data-pjax="#repo-content-pjax-container" data-turbo-frame="repo-content-turbo-frame" href="/NVIDIA/cccl/releases/tag/v2.8.0"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-tag flex-shrink-0 mt-1 color-fg-success"> <path d="M1 7.775V2.75C1 1.784 1.784 1 2.75 1h5.025c.464 0 .91.184 1.238.513l6.25 6.25a1.75 1.75 0 0 1 0 2.474l-5.026 5.026a1.75 1.75 0 0 1-2.474 0l-6.25-6.25A1.752 1.752 0 0 1 1 7.775Zm1.5 0c0 .066.026.13.073.177l6.25 6.25a.25.25 0 0 0 .354 0l5.025-5.025a.25.25 0 0 0 0-.354l-6.25-6.25a.25.25 0 0 0-.177-.073H2.75a.25.25 0 0 0-.25.25ZM6 5a1 1 0 1 1 0 2 1 1 0 0 1 0-2Z"></path> </svg> <div class="ml-2 min-width-0"> <div class="d-flex"> <span class="css-truncate css-truncate-target text-bold mr-2" style="max-width: none;">CCCL 2.8.0</span> <span title="Label: Latest" data-view-component="true" class="Label Label--success flex-shrink-0"> Latest </span> </div> <div class="text-small color-fg-muted"><relative-time datetime="2025-03-03T21:03:48Z" class="no-wrap">Mar 3, 2025</relative-time></div> </div> </a> <div data-view-component="true" class="mt-3"> <a text="small" data-pjax="#repo-content-pjax-container" data-turbo-frame="repo-content-turbo-frame" href="/NVIDIA/cccl/releases" data-view-component="true" class="Link">+ 11 releases</a></div> </div> </div> <div class="BorderGrid-row" hidden> <div class="BorderGrid-cell"> <include-fragment src="/NVIDIA/cccl/used_by_list" accept="text/fragment+html"> </include-fragment> </div> </div> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <h2 class="h4 mb-3"> <a href="/NVIDIA/cccl/graphs/contributors" data-view-component="true" class="Link--primary no-underline Link d-flex flex-items-center">Contributors <span title="95" data-view-component="true" class="Counter ml-1">95</span></a></h2> <ul class="list-style-none d-flex flex-wrap mb-n2"> <li class="mb-2 mr-2" > <a href="https://github.com/alliepiper" class="" data-hovercard-type="user" data-hovercard-url="/users/alliepiper/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/58744?s=64&v=4" alt="@alliepiper" size="32" height="32" width="32" data-view-component="true" class="avatar circle" /> </a> </li> <li class="mb-2 mr-2" > <a href="https://github.com/miscco" class="" data-hovercard-type="user" data-hovercard-url="/users/miscco/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/3017120?s=64&v=4" alt="@miscco" size="32" height="32" width="32" data-view-component="true" class="avatar circle" /> </a> </li> <li class="mb-2 mr-2" > <a href="https://github.com/jrhemstad" class="" data-hovercard-type="user" data-hovercard-url="/users/jrhemstad/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/15221289?s=64&v=4" alt="@jrhemstad" size="32" height="32" width="32" data-view-component="true" class="avatar circle" /> </a> </li> <li class="mb-2 mr-2" > <a href="https://github.com/bernhardmgruber" class="" data-hovercard-type="user" data-hovercard-url="/users/bernhardmgruber/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/1224051?s=64&v=4" alt="@bernhardmgruber" size="32" height="32" width="32" data-view-component="true" class="avatar circle" /> </a> </li> <li class="mb-2 mr-2" > <a href="https://github.com/gevtushenko" class="" data-hovercard-type="user" data-hovercard-url="/users/gevtushenko/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/9890394?s=64&v=4" alt="@gevtushenko" size="32" height="32" width="32" data-view-component="true" class="avatar circle" /> </a> </li> <li class="mb-2 mr-2" > <a href="https://github.com/ericniebler" class="" data-hovercard-type="user" data-hovercard-url="/users/ericniebler/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/225757?s=64&v=4" alt="@ericniebler" size="32" height="32" width="32" data-view-component="true" class="avatar circle" /> </a> </li> <li class="mb-2 mr-2" > <a href="https://github.com/wmaxey" class="" data-hovercard-type="user" data-hovercard-url="/users/wmaxey/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/71408887?s=64&v=4" alt="@wmaxey" size="32" height="32" width="32" data-view-component="true" class="avatar circle" /> </a> </li> <li class="mb-2 mr-2" > <a href="https://github.com/elstehle" class="" data-hovercard-type="user" data-hovercard-url="/users/elstehle/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/3958403?s=64&v=4" alt="@elstehle" size="32" height="32" width="32" data-view-component="true" class="avatar circle" /> </a> </li> <li class="mb-2 mr-2" > <a href="https://github.com/fbusato" class="" data-hovercard-type="user" data-hovercard-url="/users/fbusato/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/50413820?s=64&v=4" alt="@fbusato" size="32" height="32" width="32" data-view-component="true" class="avatar circle" /> </a> </li> <li class="mb-2 mr-2" > <a href="https://github.com/davebayer" class="" data-hovercard-type="user" data-hovercard-url="/users/davebayer/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/48736217?s=64&v=4" alt="@davebayer" size="32" height="32" width="32" data-view-component="true" class="avatar circle" /> </a> </li> <li class="mb-2 mr-2" > <a href="https://github.com/brycelelbach" class="" data-hovercard-type="user" data-hovercard-url="/users/brycelelbach/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/398194?s=64&v=4" alt="@brycelelbach" size="32" height="32" width="32" data-view-component="true" class="avatar circle" /> </a> </li> <li class="mb-2 mr-2" > <a href="https://github.com/ahendriksen" class="" data-hovercard-type="user" data-hovercard-url="/users/ahendriksen/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/4172822?s=64&v=4" alt="@ahendriksen" size="32" height="32" width="32" data-view-component="true" class="avatar circle" /> </a> </li> <li class="mb-2 mr-2" > <a href="https://github.com/caugonnet" class="" data-hovercard-type="user" data-hovercard-url="/users/caugonnet/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/158148890?s=64&v=4" alt="@caugonnet" size="32" height="32" width="32" data-view-component="true" class="avatar circle" /> </a> </li> <li class="mb-2 mr-2" > <a href="https://github.com/pciolkosz" class="" data-hovercard-type="user" data-hovercard-url="/users/pciolkosz/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/17292502?s=64&v=4" alt="@pciolkosz" size="32" height="32" width="32" data-view-component="true" class="avatar circle" /> </a> </li> </ul> <div data-view-component="true" class="mt-3"> <a text="small" href="/NVIDIA/cccl/graphs/contributors" data-view-component="true" class="Link--inTextBlock Link">+ 81 contributors</a></div> </div> </div> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <h2 class="h4 mb-3">Languages</h2> <div class="mb-2"> <span data-view-component="true" class="Progress"> <span style="background-color:#f34b7d !important;;width: 58.5%;" itemprop="keywords" data-view-component="true" class="Progress-item color-bg-success-emphasis"></span> <span style="background-color:#3A4E3A !important;;width: 32.5%;" itemprop="keywords" data-view-component="true" class="Progress-item color-bg-success-emphasis"></span> <span style="background-color:#555555 !important;;width: 5.4%;" itemprop="keywords" data-view-component="true" class="Progress-item color-bg-success-emphasis"></span> <span style="background-color:#3572A5 !important;;width: 2.0%;" itemprop="keywords" data-view-component="true" class="Progress-item color-bg-success-emphasis"></span> <span style="background-color:#DA3434 !important;;width: 1.4%;" itemprop="keywords" data-view-component="true" class="Progress-item color-bg-success-emphasis"></span> <span style="background-color:#89e051 !important;;width: 0.2%;" itemprop="keywords" data-view-component="true" class="Progress-item color-bg-success-emphasis"></span> </span></div> <ul class="list-style-none"> <li class="d-inline"> <a class="d-inline-flex flex-items-center flex-nowrap Link--secondary no-underline text-small mr-3" href="/NVIDIA/cccl/search?l=c%2B%2B" data-ga-click="Repository, language stats search click, location:repo overview"> <svg style="color:#f34b7d;" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-dot-fill mr-2"> <path d="M8 4a4 4 0 1 1 0 8 4 4 0 0 1 0-8Z"></path> </svg> <span class="color-fg-default text-bold mr-1">C++</span> <span>58.5%</span> </a> </li> <li class="d-inline"> <a class="d-inline-flex flex-items-center flex-nowrap Link--secondary no-underline text-small mr-3" href="/NVIDIA/cccl/search?l=cuda" data-ga-click="Repository, language stats search click, location:repo overview"> <svg style="color:#3A4E3A;" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-dot-fill mr-2"> <path d="M8 4a4 4 0 1 1 0 8 4 4 0 0 1 0-8Z"></path> </svg> <span class="color-fg-default text-bold mr-1">Cuda</span> <span>32.5%</span> </a> </li> <li class="d-inline"> <a class="d-inline-flex flex-items-center flex-nowrap Link--secondary no-underline text-small mr-3" href="/NVIDIA/cccl/search?l=c" data-ga-click="Repository, language stats search click, location:repo overview"> <svg style="color:#555555;" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-dot-fill mr-2"> <path d="M8 4a4 4 0 1 1 0 8 4 4 0 0 1 0-8Z"></path> </svg> <span class="color-fg-default text-bold mr-1">C</span> <span>5.4%</span> </a> </li> <li class="d-inline"> <a class="d-inline-flex flex-items-center flex-nowrap Link--secondary no-underline text-small mr-3" href="/NVIDIA/cccl/search?l=python" data-ga-click="Repository, language stats search click, location:repo overview"> <svg style="color:#3572A5;" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-dot-fill mr-2"> <path d="M8 4a4 4 0 1 1 0 8 4 4 0 0 1 0-8Z"></path> </svg> <span class="color-fg-default text-bold mr-1">Python</span> <span>2.0%</span> </a> </li> <li class="d-inline"> <a class="d-inline-flex flex-items-center flex-nowrap Link--secondary no-underline text-small mr-3" href="/NVIDIA/cccl/search?l=cmake" data-ga-click="Repository, language stats search click, location:repo overview"> <svg style="color:#DA3434;" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-dot-fill mr-2"> <path d="M8 4a4 4 0 1 1 0 8 4 4 0 0 1 0-8Z"></path> </svg> <span class="color-fg-default text-bold mr-1">CMake</span> <span>1.4%</span> </a> </li> <li class="d-inline"> <a class="d-inline-flex flex-items-center flex-nowrap Link--secondary no-underline text-small mr-3" href="/NVIDIA/cccl/search?l=shell" data-ga-click="Repository, language stats search click, location:repo overview"> <svg style="color:#89e051;" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-dot-fill mr-2"> <path d="M8 4a4 4 0 1 1 0 8 4 4 0 0 1 0-8Z"></path> </svg> <span class="color-fg-default text-bold mr-1">Shell</span> <span>0.2%</span> </a> </li> </ul> </div> </div> </div> </div> </div></div> </div> </div> </turbo-frame> </main> </div> </div> <footer class="footer pt-8 pb-6 f6 color-fg-muted p-responsive" role="contentinfo" > <h2 class='sr-only'>Footer</h2> <div class="d-flex flex-justify-center flex-items-center flex-column-reverse flex-lg-row flex-wrap flex-lg-nowrap"> <div class="d-flex flex-items-center flex-shrink-0 mx-2"> <a aria-label="Homepage" title="GitHub" class="footer-octicon mr-2" href="https://github.com"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-mark-github"> <path d="M12 1C5.9225 1 1 5.9225 1 12C1 16.8675 4.14875 20.9787 8.52125 22.4362C9.07125 22.5325 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data-analytics-event="{"location":"footer","action":"cookies","context":"subfooter","tag":"link","label":"cookies_link_subfooter_footer"}" > Manage cookies </button> </cookie-consent-link> </li> <li class="mx-2"> <cookie-consent-link> <button type="button" class="Link--secondary underline-on-hover border-0 p-0 color-bg-transparent" data-action="click:cookie-consent-link#showConsentManagement" data-analytics-event="{"location":"footer","action":"dont_share_info","context":"subfooter","tag":"link","label":"dont_share_info_link_subfooter_footer"}" > Do not share my personal information </button> </cookie-consent-link> </li> </ul> </nav> </div> </footer> <ghcc-consent id="ghcc" class="position-fixed bottom-0 left-0" style="z-index: 999999" data-initial-cookie-consent-allowed="" data-cookie-consent-required="false"></ghcc-consent> <div id="ajax-error-message" class="ajax-error-message flash flash-error" hidden> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-alert"> <path d="M6.457 1.047c.659-1.234 2.427-1.234 3.086 0l6.082 11.378A1.75 1.75 0 0 1 14.082 15H1.918a1.75 1.75 0 0 1-1.543-2.575Zm1.763.707a.25.25 0 0 0-.44 0L1.698 13.132a.25.25 0 0 0 .22.368h12.164a.25.25 0 0 0 .22-.368Zm.53 3.996v2.5a.75.75 0 0 1-1.5 0v-2.5a.75.75 0 0 1 1.5 0ZM9 11a1 1 0 1 1-2 0 1 1 0 0 1 2 0Z"></path> </svg> <button type="button" class="flash-close js-ajax-error-dismiss" aria-label="Dismiss error"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-x"> <path d="M3.72 3.72a.75.75 0 0 1 1.06 0L8 6.94l3.22-3.22a.749.749 0 0 1 1.275.326.749.749 0 0 1-.215.734L9.06 8l3.22 3.22a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215L8 9.06l-3.22 3.22a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042L6.94 8 3.72 4.78a.75.75 0 0 1 0-1.06Z"></path> </svg> </button> You can’t perform that action at this time. </div> <template id="site-details-dialog"> <details class="details-reset details-overlay details-overlay-dark lh-default color-fg-default hx_rsm" open> <summary role="button" aria-label="Close dialog"></summary> <details-dialog class="Box Box--overlay d-flex flex-column anim-fade-in fast hx_rsm-dialog hx_rsm-modal"> <button class="Box-btn-octicon m-0 btn-octicon position-absolute right-0 top-0" type="button" aria-label="Close dialog" data-close-dialog> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-x"> <path d="M3.72 3.72a.75.75 0 0 1 1.06 0L8 6.94l3.22-3.22a.749.749 0 0 1 1.275.326.749.749 0 0 1-.215.734L9.06 8l3.22 3.22a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215L8 9.06l-3.22 3.22a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042L6.94 8 3.72 4.78a.75.75 0 0 1 0-1.06Z"></path> </svg> </button> <div class="octocat-spinner my-6 js-details-dialog-spinner"></div> </details-dialog> </details> </template> <div class="Popover js-hovercard-content position-absolute" style="display: none; outline: none;"> <div class="Popover-message Popover-message--bottom-left Popover-message--large Box color-shadow-large" style="width:360px;"> </div> </div> <template id="snippet-clipboard-copy-button"> <div class="zeroclipboard-container position-absolute right-0 top-0"> <clipboard-copy aria-label="Copy" class="ClipboardButton btn js-clipboard-copy m-2 p-0" data-copy-feedback="Copied!" data-tooltip-direction="w"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-copy js-clipboard-copy-icon m-2"> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 0 1 0 1.5h-1.5a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-1.5a.75.75 0 0 1 1.5 0v1.5A1.75 1.75 0 0 1 9.25 16h-7.5A1.75 1.75 0 0 1 0 14.25Z"></path><path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0 1 14.25 11h-7.5A1.75 1.75 0 0 1 5 9.25Zm1.75-.25a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-7.5a.25.25 0 0 0-.25-.25Z"></path> </svg> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-check js-clipboard-check-icon color-fg-success d-none m-2"> <path d="M13.78 4.22a.75.75 0 0 1 0 1.06l-7.25 7.25a.75.75 0 0 1-1.06 0L2.22 9.28a.751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018L6 10.94l6.72-6.72a.75.75 0 0 1 1.06 0Z"></path> </svg> </clipboard-copy> </div> </template> <template id="snippet-clipboard-copy-button-unpositioned"> <div class="zeroclipboard-container"> <clipboard-copy aria-label="Copy" class="ClipboardButton btn btn-invisible js-clipboard-copy m-2 p-0 d-flex flex-justify-center flex-items-center" data-copy-feedback="Copied!" data-tooltip-direction="w"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-copy js-clipboard-copy-icon"> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 0 1 0 1.5h-1.5a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-1.5a.75.75 0 0 1 1.5 0v1.5A1.75 1.75 0 0 1 9.25 16h-7.5A1.75 1.75 0 0 1 0 14.25Z"></path><path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0 1 14.25 11h-7.5A1.75 1.75 0 0 1 5 9.25Zm1.75-.25a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-7.5a.25.25 0 0 0-.25-.25Z"></path> </svg> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-check js-clipboard-check-icon color-fg-success d-none"> <path d="M13.78 4.22a.75.75 0 0 1 0 1.06l-7.25 7.25a.75.75 0 0 1-1.06 0L2.22 9.28a.751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018L6 10.94l6.72-6.72a.75.75 0 0 1 1.06 0Z"></path> </svg> </clipboard-copy> </div> </template> </div> <div id="js-global-screen-reader-notice" class="sr-only mt-n1" aria-live="polite" aria-atomic="true" ></div> <div id="js-global-screen-reader-notice-assertive" class="sr-only mt-n1" aria-live="assertive" aria-atomic="true"></div> </body> </html>