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GitHub - NVIDIA/cutlass: CUDA Templates for Linear Algebra Subroutines
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<nav class="HeaderMenu-nav" aria-label="Global"> <ul class="d-lg-flex list-style-none"> <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"> Product <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 octicon-chevron-down HeaderMenu-icon ml-1"> <path d="M12.78 5.22a.749.749 0 0 1 0 1.06l-4.25 4.25a.749.749 0 0 1-1.06 0L3.22 6.28a.749.749 0 1 1 1.06-1.06L8 8.939l3.72-3.719a.749.749 0 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 d-lg-flex 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1.132-3.368.397-.406.89-.717 1.474-.952 1.399-1.136 3.392-2.093 6.122-2.093 2.731 0 4.767.957 6.166 2.093.584.235 1.077.546 1.474.952.85.869 1.132 2.037 1.132 3.368 0 .368-.014.733-.052 1.086.23.462.477 1.088.644 1.517 1.258.364 2.233 1.721 2.605 2.656a.832.832 0 0 1 .053.22v2.869a.641.641 0 0 1-.078.256ZM12.172 11h-.344a4.323 4.323 0 0 1-.355.508C10.703 12.455 9.555 13 7.965 13c-1.725 0-2.989-.359-3.782-1.259a2.005 2.005 0 0 1-.085-.104L4 11.741v6.585c1.435.779 4.514 2.179 8 2.179 3.486 0 6.565-1.4 8-2.179v-6.585l-.098-.104s-.033.045-.085.104c-.793.9-2.057 1.259-3.782 1.259-1.59 0-2.738-.545-3.508-1.492a4.323 4.323 0 0 1-.355-.508h-.016.016Zm.641-2.935c.136 1.057.403 1.913.878 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" data-analytics-event="{"location":"navbar","action":"security","context":"product","tag":"link","label":"security_link_product_navbar"}" href="https://github.com/features/security"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon 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data-analytics-event="{"location":"navbar","action":"codespaces","context":"product","tag":"link","label":"codespaces_link_product_navbar"}" href="https://github.com/features/codespaces"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-codespaces color-fg-subtle mr-3"> <path d="M3.5 3.75C3.5 2.784 4.284 2 5.25 2h13.5c.966 0 1.75.784 1.75 1.75v7.5A1.75 1.75 0 0 1 18.75 13H5.25a1.75 1.75 0 0 1-1.75-1.75Zm-2 12c0-.966.784-1.75 1.75-1.75h17.5c.966 0 1.75.784 1.75 1.75v4a1.75 1.75 0 0 1-1.75 1.75H3.25a1.75 1.75 0 0 1-1.75-1.75ZM5.25 3.5a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h13.5a.25.25 0 0 0 .25-.25v-7.5a.25.25 0 0 0-.25-.25Zm-2 12a.25.25 0 0 0-.25.25v4c0 .138.112.25.25.25h17.5a.25.25 0 0 0 .25-.25v-4a.25.25 0 0 0-.25-.25Z"></path><path d="M10 17.75a.75.75 0 0 1 .75-.75h6.5a.75.75 0 0 1 0 1.5h-6.5a.75.75 0 0 1-.75-.75Zm-4 0a.75.75 0 0 1 .75-.75h.5a.75.75 0 0 1 0 1.5h-.5a.75.75 0 0 1-.75-.75Z"></path> 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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> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary Link--external" target="_blank" data-analytics-event="{"location":"navbar","action":"github_skills","context":"product","tag":"link","label":"github_skills_link_product_navbar"}" href="https://skills.github.com"> GitHub Skills <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-link-external HeaderMenu-external-icon color-fg-subtle"> <path d="M3.75 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 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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 octicon-chevron-down HeaderMenu-icon ml-1"> <path d="M12.78 5.22a.749.749 0 0 1 0 1.06l-4.25 4.25a.749.749 0 0 1-1.06 0L3.22 6.28a.749.749 0 1 1 1.06-1.06L8 8.939l3.72-3.719a.749.749 0 0 1 1.06 0Z"></path> </svg> </button> <div class="HeaderMenu-dropdown dropdown-menu rounded m-0 p-0 pt-2 pt-lg-4 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height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-chevron-down HeaderMenu-icon ml-1"> <path d="M12.78 5.22a.749.749 0 0 1 0 1.06l-4.25 4.25a.749.749 0 0 1-1.06 0L3.22 6.28a.749.749 0 1 1 1.06-1.06L8 8.939l3.72-3.719a.749.749 0 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> 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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"> Enterprise <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 octicon-chevron-down HeaderMenu-icon ml-1"> <path d="M12.78 5.22a.749.749 0 0 1 0 1.06l-4.25 4.25a.749.749 0 0 1-1.06 0L3.22 6.28a.749.749 0 1 1 1.06-1.06L8 8.939l3.72-3.719a.749.749 0 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 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4.192a.751.751 0 0 1-1.088.791L8 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><span data-view-component="true" class="d-inline"> Star </span> </a></div> </div> <div class="flex-1"> <a href="/login?return_to=%2FNVIDIA%2Fcutlass" rel="nofollow" id="files-overview-watch-button" data-hydro-click="{"event_type":"authentication.click","payload":{"location_in_page":"notification subscription menu watch","repository_id":null,"auth_type":"LOG_IN","originating_url":"https://github.com/NVIDIA/cutlass","user_id":null}}" data-hydro-click-hmac="85f0d564b4cea7e2900eda4d7b3f08d82f1381ae1125e9cdc5e876b1bbcfac86" aria-label="You must be signed in to change notification settings" 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href=\"/NVIDIA/cutlass/blob/main/media/images/gemm-hierarchy-with-epilogue-no-labels.png\"\u003e\u003cimg src=\"/NVIDIA/cutlass/raw/main/media/images/gemm-hierarchy-with-epilogue-no-labels.png\" alt=\"ALT\" title=\"Complete CUDA GEMM decomposition\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCUTLASS 3.8.0\u003c/h1\u003e\u003ca id=\"user-content-cutlass-380\" class=\"anchor\" aria-label=\"Permalink: CUTLASS 3.8.0\" href=\"#cutlass-380\"\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\u003cem\u003eCUTLASS 3.8.0 - January 2025\u003c/em\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eCUTLASS is a collection of CUDA C++ template abstractions for implementing\nhigh-performance matrix-matrix multiplication (GEMM) and related computations at all levels\nand scales within CUDA. It incorporates strategies for hierarchical decomposition and\ndata movement similar to those used to implement cuBLAS and cuDNN. CUTLASS decomposes\nthese \"moving parts\" into reusable, modular software components abstracted by C++ template\nclasses. Primitives for different levels of a conceptual parallelization hierarchy\ncan be specialized and tuned via custom tiling sizes, data types,\nand other algorithmic policy. The resulting flexibility simplifies their use\nas building blocks within custom kernels and applications.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eTo support a wide variety of applications, CUTLASS provides extensive support for\nmixed-precision computations, providing specialized data-movement and\nmultiply-accumulate abstractions for FP64, FP32, TF32, FP16, BF16,\n\u003ca href=\"/NVIDIA/cutlass/blob/main/examples/27_ampere_3xtf32_fast_accurate_tensorop_gemm\"\u003eFP32 emulation via tensor core instruction\u003c/a\u003e,\n8b floating point types (e5m2 and e4m3),\nblock scaled data types (NVIDIA NVFP4 and OCP standard MXFP4, MXFP6, MXFP8),\nnarrow integer types (4 and 8b signed and unsigned integers),\nand binary 1b data types (where architectures allow for the\nnative support of such data types).\nCUTLASS demonstrates optimal matrix multiply operations\ntargeting the programmable, high-throughput \u003cem\u003eTensor Cores\u003c/em\u003e implemented by\nNVIDIA's Volta, Turing, Ampere, Ada, Hopper, and Blackwell architectures.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eIn addition to GEMMs, CUTLASS implements high-performance convolution via\nthe implicit GEMM algorithm. Implicit GEMM is the formulation of a convolution\noperation as a GEMM thereby taking advantage of CUTLASS's modular GEMM pipeline.\nThis allows CUTLASS to build convolutions by reusing highly-optimized GEMM components.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eSee the \u003ca href=\"/NVIDIA/cutlass/blob/main/media/docs/quickstart.md\"\u003eQuick Start Guide\u003c/a\u003e to get started quickly.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eSee the \u003ca href=\"/NVIDIA/cutlass/blob/main/media/docs/functionality.md\"\u003efunctionality docs\u003c/a\u003e for a more comprehensive\nlist of kernel level features, data types, instructions, and minimum supported by CUTLASS on each GPU\narchitecture.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eWhat's New in CUTLASS 3.8\u003c/h1\u003e\u003ca id=\"user-content-whats-new-in-cutlass-38\" class=\"anchor\" aria-label=\"Permalink: What's New in CUTLASS 3.8\" href=\"#whats-new-in-cutlass-38\"\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\"\u003eCUTLASS 3.8 is the first release that supports the NVIDIA Blackwell SM100 architecture.\nFor a background on Blackwell's new features, please consult the PTX documentation for CUDA 12.8.\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eSupport for new CuTe building blocks specifically for Blackwell architecture:\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"/NVIDIA/cutlass/blob/main/include/cute/atom/mma_traits_sm100.hpp\"\u003e5th generation Blackwell Tensor Core instructions (TCGen05)\u003c/a\u003e via CuTe MMA atoms.\u003c/li\u003e\n\u003cli\u003eExtensions to \u003ca href=\"/NVIDIA/cutlass/blob/main/include/cute/atom/copy_traits_sm100_tma.hpp\"\u003eTensor Memory Accelerator\u003c/a\u003e via CuTe Copy atoms.\u003c/li\u003e\n\u003cli\u003eExposure of Blackwell's new tensor memory (note: distinct from TMA) as \u003ca href=\"/NVIDIA/cutlass/blob/main/include/cute/pointer.hpp#L290\"\u003e\u003ccode\u003etmem\u003c/code\u003e\u003c/a\u003e across CuTe as a first class data locale.\u003c/li\u003e\n\u003cli\u003eExposure of \u003ca href=\"/NVIDIA/cutlass/blob/main/include/cute/atom/copy_traits_sm100.hpp\"\u003e\u003ccode\u003etmem-\u0026gt;rmem\u003c/code\u003e, \u003ccode\u003ermem-\u0026gt;tmem\u003c/code\u003e and \u003ccode\u003esmem-\u0026gt;tmem data movement instructions\u003c/code\u003e\u003c/a\u003e as copy atoms in CuTe.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/NVIDIA/cutlass/blob/main/include/cute/atom/copy_traits_sm100.hpp\"\u003e\u003ccode\u003emake_tmem_copy()\u003c/code\u003e\u003c/a\u003e utility method to ease creation of tiled copies for tmem copy atoms.\u003c/li\u003e\n\u003cli\u003eSupport for \u003ca href=\"/NVIDIA/cutlass/blob/main/include/cute/atom/copy_traits_sm100.hpp\"\u003enew variants of LDSM on Blackwell\u003c/a\u003e via CuTe Copy atoms.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eSupport for new CUTLASS building blocks specifically for Blackwell architecture:\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eVarious narrow precision \u003ca href=\"/NVIDIA/cutlass/blob/main/include/cutlass/exmy_base.h\"\u003eFP4, FP6, and FP8\u003c/a\u003e formats as well as their \u003ca href=\"/NVIDIA/cutlass/blob/main/include/cutlass/float_subbyte.h\"\u003eblock-scaled variants NVFP4, MXFP4, MXFP6, and MXFP8\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/NVIDIA/cutlass/blob/main/include/cutlass/pipeline/sm100_pipeline.hpp\"\u003ePipelines that implement Blackwell specific synchronization\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/NVIDIA/cutlass/blob/main/include/cutlass/cluster_launch.hpp\"\u003eCluster launch control API supporting preferred and fallback cluster shapes\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eData types including NVFP4, MXFP4, MXFP6, and MXFP8 and all their supported element and scale factor types.\u003c/li\u003e\n\u003cli\u003eTile schedulers using \u003ca href=\"/NVIDIA/cutlass/blob/main/media/docs/blackwell_cluster_launch_control.md\"\u003eBlackwell's Cluster Launch Control (CLC) feature\u003c/a\u003e to implement dynamic persistence scheduling for \u003ca href=\"/NVIDIA/cutlass/blob/main/include/cutlass/gemm/kernel/sm100_tile_scheduler.hpp\"\u003eGEMMs\u003c/a\u003e, and \u003ca href=\"/NVIDIA/cutlass/blob/main/include/cutlass/gemm/kernel/sm100_tile_scheduler_stream_k.hpp\"\u003estream-K\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eExtensions to testbeds and reference check code for unit tests and CUTLASS profiler.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eFull support for Blackwell kernels in CUTLASS 3.x API:\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"/NVIDIA/cutlass/blob/main/include/cutlass/gemm/kernel/sm100_gemm_tma_warpspecialized.hpp\"\u003eBlackwell specific kernel layers\u003c/a\u003e that\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eImplement a new warp-specialization recipe tuned specifically for Blackwell.\u003c/li\u003e\n\u003cli\u003eLeverage all the new features such as CLC based tile scheduling, preferred cluster, and TMEM based double buffering of accumulators.\u003c/li\u003e\n\u003cli\u003eSupport stream-K load balancing for all kernel types everywhere via composable scheduler support.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eBlackwell collective mainloops that target the TCGen05 MMA instructions (both SS and TS) for\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"/NVIDIA/cutlass/blob/main/include/cutlass/gemm/collective/sm100_mma_warpspecialized.hpp\"\u003eNon-block scaled data types without support for pointer array and grouped GEMM with TMA\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/NVIDIA/cutlass/blob/main/include/cutlass/gemm/collective/sm100_mma_array_warpspecialized.hpp\"\u003eNon-block scaled data types with support for pointer array and grouped GEMM with TMA\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/NVIDIA/cutlass/blob/main/include/cutlass/gemm/collective/sm100_blockscaled_mma_warpspecialized.hpp\"\u003eBlock scaled data types without support for pointer array and grouped GEMM with TMA\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/NVIDIA/cutlass/blob/main/include/cutlass/gemm/collective/sm100_blockscaled_mma_array_warpspecialized.hpp\"\u003eBlock scaled data types with support for pointer array and grouped GEMM with TMA\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eBlackwell \u003ca href=\"/NVIDIA/cutlass/blob/main/include/cutlass/conv/collective/sm100_implicit_gemm_umma_warpspecialized.hpp\"\u003ecollective mainloop for convolution kernels\u003c/a\u003e supporting non-block scaled data types for fprop, dgrad, and wgrad.\u003c/li\u003e\n\u003cli\u003eNew \u003ca href=\"/NVIDIA/cutlass/blob/main/include/cutlass/gemm/dispatch_policy.hpp\"\u003eGEMM\u003c/a\u003e, \u003ca href=\"/NVIDIA/cutlass/blob/main/include/cutlass/conv/dispatch_policy.hpp\"\u003econvolution\u003c/a\u003e, and \u003ca href=\"/NVIDIA/cutlass/blob/main/include/cutlass/epilogue/dispatch_policy.hpp\"\u003eepilogue\u003c/a\u003e dispatch policies for collectives, kernel layers, and builders.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/NVIDIA/cutlass/blob/main/include/cutlass/epilogue/collective/sm100_epilogue_tma_warpspecialized.hpp\"\u003eBlackwell epilogue that supports loading accumulators from \u003ccode\u003etmem\u003c/code\u003e\u003c/a\u003e and \u003ca href=\"/NVIDIA/cutlass/blob/main\"\u003efull set of EVT fusions\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eCUTLASS library and profiler integration for block scaled data types for kernel emission, profiling, and verification.\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eSupport for preferred and fallback cluster shapes via profiler command line arguments parsing to set dynamic cluster shapes.\u003c/li\u003e\n\u003cli\u003eSupport for dynamic datatypes by parsing profiler via profiler command line arguments parsing to set dynamic datatype setting in TCGen05 MMA instruction descriptors.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eSet of examples that demonstrate the usage of the 3.x API for targeting Blackwell\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"/NVIDIA/cutlass/blob/main/examples/70_blackwell_gemm\"\u003eBasic FP16 and FP8 GEMMs with minimal changes from Hopper examples\u003c/a\u003e, demonstrating ease of migration for off the shelf kernels using the 3.x collective builder API.\u003c/li\u003e\n\u003cli\u003eGEMM with \u003ca href=\"/NVIDIA/cutlass/blob/main/examples/71_blackwell_gemm_with_collective_builder/71_blackwell_gemm_with_collective_builder.cu\"\u003eopt-in collective builder schedules showcasing available recipes\u003c/a\u003e for Blackwell.\u003c/li\u003e\n\u003cli\u003eBlock scaled data type GEMMs targeting Blackwell's native block scaled Tensor Cores:\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"/NVIDIA/cutlass/blob/main/examples/72_blackwell_narrow_precision_gemm/72a_blackwell_nvfp4_bf16_gemm.cu\"\u003eNVFP4 inputs with BF16 output\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/NVIDIA/cutlass/blob/main/examples/72_blackwell_narrow_precision_gemm/72b_blackwell_nvfp4_nvfp4_gemm.cu\"\u003eNVFP4 inputs with NVFP4 output\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/NVIDIA/cutlass/blob/main/examples/72_blackwell_narrow_precision_gemm/72c_blackwell_mixed_mxfp8_bf16_gemm.cu\"\u003eMixed MXFP8 and MXFP6 inputs with BF16 output\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eGEMM example demonstrating \u003ca href=\"/NVIDIA/cutlass/blob/main/examples/73_blackwell_gemm_preferred_cluster/blackwell_gemm_preferred_cluster.cu\"\u003eBlackwell's new preferred cluster support via dynamic cluster shapes\u003c/a\u003e for increased occupancy.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/NVIDIA/cutlass/blob/main/examples/74_blackwell_gemm_streamk/blackwell_gemm_streamk.cu\"\u003eGEMM with CLC based StreamK scheduler for load balancing\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eGrouped GEMM for \u003ca href=\"/NVIDIA/cutlass/blob/main/examples/75_blackwell_grouped_gemm/75_blackwell_grouped_gemm.cu\"\u003evanilla FP8 data inputs\u003c/a\u003e and \u003ca href=\"/NVIDIA/cutlass/blob/main/examples/75_blackwell_grouped_gemm/75_blackwell_grouped_gemm_block_scaled.cu\"\u003eNVFP4 block scaled inputs\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eConvolution kernels for \u003ca href=\"/NVIDIA/cutlass/blob/main/examples/76_blackwell_conv/76_blackwell_conv_fprop.cu\"\u003efprop\u003c/a\u003e, \u003ca href=\"/NVIDIA/cutlass/blob/main/examples/76_blackwell_conv/76_blackwell_conv_dgrad.cu\"\u003edgrad\u003c/a\u003e, and \u003ca href=\"/NVIDIA/cutlass/blob/main/examples/76_blackwell_conv/76_blackwell_conv_wgrad.cu\"\u003ewgrad\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/NVIDIA/cutlass/blob/main/examples/77_blackwell_fmha/77_blackwell_fmha.cu\"\u003eFused multi-head attention fprop kernel\u003c/a\u003e supporting fp16/bf16/fp8 data types across head dims of 32,64, and 128.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eDocumentation updates:\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"/NVIDIA/cutlass/blob/main/media/docs/quickstart.md#instantiating-a-blackwell-gemm-kernel\"\u003eQuickstart - instantiating a Blackwell block-scaled GEMM\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eDetailed \u003ca href=\"/NVIDIA/cutlass/blob/main/media/docs/blackwell_functionality.md\"\u003eBlackwell block-scaled GEMM functionality documentation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eA new \u003ca href=\"/NVIDIA/cutlass/blob/main/media/docs/functionality.md\"\u003efunctionality documentation\u003c/a\u003e specifically for 3.x API comprehensively documenting all supported kernel types, data types, kernel features, minimum CUDA tookit support etc for 3.x supported architectures.\u003c/li\u003e\n\u003cli\u003eUpdates to \u003ca href=\"/NVIDIA/cutlass/blob/main/README.md#compatibility\"\u003ecompatibility\u003c/a\u003e section regarding supported compilers, operating systems, CUDA Toolkits, Hardware Architectures, and \u003ca href=\"/NVIDIA/cutlass/blob/main/README.md#Target-Architecture\"\u003eTarget Architecture\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eNote: CUTLASS 3.x builds are known to be down on Windows platforms for all CUDA toolkits.\nCUTLASS team is working on a fix.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eSee the \u003ca href=\"/NVIDIA/cutlass/blob/main/CHANGELOG.md\"\u003eCHANGELOG\u003c/a\u003e for details of all past releases and updates.\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003ePerformance\u003c/h1\u003e\u003ca id=\"user-content-performance\" class=\"anchor\" aria-label=\"Permalink: Performance\" href=\"#performance\"\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\"\u003eCUTLASS primitives are very efficient. When used to construct device-wide GEMM kernels,\nthey exhibit nearly optimal utilization of peak theoretical throughput. The figure below\nshows CUTLASS 3.8's performance as a % of theoretical peak utilization\non various input and output data types when run on NVIDIA Blackwell SM100 architecture GPU.\u003c/p\u003e\n\u003cp align=\"center\" dir=\"auto\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/NVIDIA/cutlass/blob/main/media/images/cutlass-3.8-blackwell-gemm-peak-performance.svg\"\u003e\u003cimg src=\"/NVIDIA/cutlass/raw/main/media/images/cutlass-3.8-blackwell-gemm-peak-performance.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThe two figures below show the continual CUTLASS performance improvements\non an \u003ca href=\"https://www.nvidia.com/en-us/data-center/h100/\" rel=\"nofollow\"\u003eNVIDIA H100\u003c/a\u003e (NVIDIA Hopper architecture) since\nCUTLASS 3.1.\nCUTLASS 3.5.1 was compiled with the \u003ca href=\"https://developer.nvidia.com/cuda-downloads\" rel=\"nofollow\"\u003eCUDA 12.5u1 Toolkit\u003c/a\u003e.\nTensor Core operations are implemented using CUDA's\n\u003ca href=\"https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#warp-level-matrix-instructions-mma\" rel=\"nofollow\"\u003emma\u003c/a\u003e and\n\u003ca href=\"https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#asynchronous-warpgroup-level-matrix-instructions\" rel=\"nofollow\"\u003ewgmma\u003c/a\u003e instructions.\u003c/p\u003e\n\u003cp align=\"center\" dir=\"auto\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/NVIDIA/cutlass/blob/main/media/images/cutlass-3.5.1-gemm-peak-performance.png\"\u003e\u003cimg src=\"/NVIDIA/cutlass/raw/main/media/images/cutlass-3.5.1-gemm-peak-performance.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp align=\"center\" dir=\"auto\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/NVIDIA/cutlass/blob/main/media/images/cutlass-3.5.1-gemm-peak-performance-fp8.png\"\u003e\u003cimg src=\"/NVIDIA/cutlass/raw/main/media/images/cutlass-3.5.1-gemm-peak-performance-fp8.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCuTe\u003c/h1\u003e\u003ca id=\"user-content-cute\" class=\"anchor\" aria-label=\"Permalink: CuTe\" href=\"#cute\"\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\"\u003eCUTLASS 3.0 introduced a new core library, CuTe, to describe and manipulate tensors of threads and data.\nCuTe is a collection of C++ CUDA template abstractions for\ndefining and operating on hierarchically multidimensional layouts of threads and data.\nCuTe provides \u003ccode\u003eLayout\u003c/code\u003e and \u003ccode\u003eTensor\u003c/code\u003e objects that compactly package the type,\nshape, memory space, and layout of data, while performing the complicated indexing for the user.\nThis lets programmers focus on the logical descriptions of their algorithms while\nCuTe does the mechanical bookkeeping for them. With these tools, we can quickly design,\nimplement, and modify all dense linear algebra operations.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThe core abstractions of CuTe are hierarchically multidimensional layouts\nwhich can be composed with data arrays to represent tensors.\nThe representation of layouts is powerful enough to represent nearly\neverything we need to implement efficient dense linear algebra.\nLayouts can also be combined and manipulated via functional composition, on which we build a large set of common operations such as tiling and partitioning.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eCUTLASS 3.0 and beyond adopts CuTe throughout the GEMM hierarchy in its templates.\nThis greatly simplifies the design and improves code composability and readability.\nMore documentation specific to CuTe can be found in its\n\u003ca href=\"/NVIDIA/cutlass/blob/main/media/docs/cute/00_quickstart.md\"\u003ededicated documentation directory\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCompatibility\u003c/h1\u003e\u003ca id=\"user-content-compatibility\" class=\"anchor\" aria-label=\"Permalink: Compatibility\" href=\"#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\"\u003eMinimum requirements:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eArchitecture: Volta (compute capability 7.0)\u003c/li\u003e\n\u003cli\u003eCompiler: Must support at least C++17\u003c/li\u003e\n\u003cli\u003eCUDA Toolkit version: 11.4\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eCUTLASS requires a C++17 host compiler and\nperforms best when built with the \u003ca href=\"https://developer.nvidia.com/cuda-downloads\" rel=\"nofollow\"\u003e\u003cstrong\u003eCUDA 12.8 Toolkit\u003c/strong\u003e\u003c/a\u003e.\nIt is also compatible with CUDA 11.4, CUDA 11.5, CUDA 11.6, CUDA 11.7, CUDA 11.8, and all other CUDA 12.x versions.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eOperating Systems\u003c/h2\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\"\u003eWe have tested the following environments.\u003c/p\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003cstrong\u003eOperating System\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003eCompiler\u003c/strong\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 18.04\u003c/td\u003e\n\u003ctd\u003eGCC 7.5.0\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 20.04\u003c/td\u003e\n\u003ctd\u003eGCC 10.3.0\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eUbuntu 22.04\u003c/td\u003e\n\u003ctd\u003eGCC 11.2.0\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cp dir=\"auto\"\u003eNote: GCC 8.5.0 has known regressions regarding fold expressions and overloaded operators. Using GCC 7.5.0 or (preferred) GCC \u0026gt;= 9 is recommended.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eNote: CUTLASS 3.x builds are known to be down on Windows platforms for all CUDA toolkits.\nCUTLASS team is working on a fix.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eHardware\u003c/h2\u003e\u003ca id=\"user-content-hardware\" class=\"anchor\" aria-label=\"Permalink: Hardware\" href=\"#hardware\"\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\"\u003eCUTLASS runs successfully on the following NVIDIA GPUs, and it is expected to be efficient on Volta, Turing, Ampere, Ada, and Hopper architecture based NVIDIA GPUs.\u003c/p\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003cstrong\u003eGPU\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003eCUDA Compute Capability\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003eMinimum CUDA Toolkit Required by CUTLASS-3\u003c/strong\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eNVIDIA V100 Tensor Core GPU\u003c/td\u003e\n\u003ctd\u003e7.0\u003c/td\u003e\n\u003ctd\u003e11.4\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eNVIDIA TitanV\u003c/td\u003e\n\u003ctd\u003e7.0\u003c/td\u003e\n\u003ctd\u003e11.4\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eNVIDIA GeForce RTX 20x0 series\u003c/td\u003e\n\u003ctd\u003e7.5\u003c/td\u003e\n\u003ctd\u003e11.4\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eNVIDIA T4\u003c/td\u003e\n\u003ctd\u003e7.5\u003c/td\u003e\n\u003ctd\u003e11.4\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eNVIDIA A100 Tensor Core GPU\u003c/td\u003e\n\u003ctd\u003e8.0\u003c/td\u003e\n\u003ctd\u003e11.4\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eNVIDIA A10\u003c/td\u003e\n\u003ctd\u003e8.6\u003c/td\u003e\n\u003ctd\u003e11.4\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eNVIDIA GeForce RTX 30x0 series\u003c/td\u003e\n\u003ctd\u003e8.6\u003c/td\u003e\n\u003ctd\u003e11.4\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eNVIDIA GeForce RTX 40x0 series\u003c/td\u003e\n\u003ctd\u003e8.9\u003c/td\u003e\n\u003ctd\u003e11.8\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eNVIDIA L40\u003c/td\u003e\n\u003ctd\u003e8.9\u003c/td\u003e\n\u003ctd\u003e11.8\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eNVIDIA H100 Tensor Core GPU\u003c/td\u003e\n\u003ctd\u003e9.0\u003c/td\u003e\n\u003ctd\u003e11.8\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eNVIDIA H200 Tensor Core GPU\u003c/td\u003e\n\u003ctd\u003e9.0\u003c/td\u003e\n\u003ctd\u003e11.8\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eNVIDIA B200 Tensor Core GPU\u003c/td\u003e\n\u003ctd\u003e10.0\u003c/td\u003e\n\u003ctd\u003e12.8\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\"\u003eTarget Architecture\u003c/h2\u003e\u003ca id=\"user-content-target-architecture\" class=\"anchor\" aria-label=\"Permalink: Target Architecture\" href=\"#target-architecture\"\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\"\u003eIn general, PTX code generated for one target architecture can be run on future architectures\n(i.e., it is forward compatible).\nHowever, CUDA 12.0 introduced the concept of \"architecture-accelerated features\" whose\nPTX does not have forward compatibility guarantees.\nSeveral Hopper and Blackwell PTX instructions fall under this category of\narchitecture-accelerated features, and thus require a \u003ccode\u003esm_90a\u003c/code\u003e or \u003ccode\u003esm100a\u003c/code\u003e target architecture\n(note the \"a\" appended). For more details on this and other architecture-accelerated instructions,\nplease refer to the \u003ca href=\"https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#feature-availability\" rel=\"nofollow\"\u003eCUDA Documentation\u003c/a\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThe target architecture information is passed on to CUTLASS via the cmake flag\n\u003ccode\u003eCUTLASS_NVCC_ARCHS\u003c/code\u003e. In order to maximize performance on Hopper GH100,\nusers are required to build CUTLASS with \u003ccode\u003e90a\u003c/code\u003e as the target architecture.\nIf a user accidentally builds a kernel which uses SM90a features\n(e.g. Hopper Tensor Core Instructions), using the SM90 target\n(note the lack of \"a\"), with either CUDA Toolkit 12 or 11.8,\nthe kernel is expected to fail with a runtime error.\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"cmake .. -DCUTLASS_NVCC_ARCHS=\u0026quot;90a\u0026quot;\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003ecmake .. -DCUTLASS_NVCC_ARCHS=\"90a\"\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eOr\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"cmake .. -DCUTLASS_NVCC_ARCHS=\u0026quot;100a\u0026quot; \"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003ecmake .. -DCUTLASS_NVCC_ARCHS=\"100a\" \n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eNote: The NVIDIA Blackwell SM100 architecture used in the datacenter\nproducts has a different compute capability than the one underpinning\nNVIDIA Blackwell GeForce RTX 50 series GPUs. As a result, kernels\ncompiled for Blackwell SM100 architecture with arch conditional features\n(using \u003ccode\u003esm100a\u003c/code\u003e) are not compatible with RTX 50 series GPUs.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003ePlease refer to the \u003ca href=\"/NVIDIA/cutlass/blob/main/media/docs/functionality.md\"\u003efunctionality documentation\u003c/a\u003e\nfor details on which kernels require which target architectures.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eDocumentation\u003c/h1\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\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\"\u003eCUTLASS is described in the following documents and the accompanying\n\u003ca href=\"https://nvidia.github.io/cutlass\" rel=\"nofollow\"\u003eDoxygen documentation\u003c/a\u003e.\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"/NVIDIA/cutlass/blob/main/media/docs/quickstart.md\"\u003eQuick Start Guide\u003c/a\u003e - basics of building and running CUTLASS\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/NVIDIA/cutlass/blob/main/media/docs/functionality.md\"\u003eFunctionality\u003c/a\u003e - summarizes functionality available in CUTLASS\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/NVIDIA/cutlass/blob/main/media/docs/efficient_gemm.md\"\u003eEfficient GEMM in CUDA\u003c/a\u003e - describes how GEMM kernels may be implemented efficiently in CUDA\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/NVIDIA/cutlass/blob/main/media/docs/cutlass_3x_design.md\"\u003eCUTLASS 3.x Design\u003c/a\u003e - describes the CUTLASS 3.x design, its benefits, and how CuTe enables us to write much more composable components\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/NVIDIA/cutlass/blob/main/media/docs/gemm_api_3x.md\"\u003eGEMM API 3.x\u003c/a\u003e - describes the CUTLASS 3.x GEMM model and C++ template concepts\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/NVIDIA/cutlass/blob/main/media/docs/gemm_api.md\"\u003eGEMM API 2.x\u003c/a\u003e - describes the CUTLASS 2.x GEMM model and C++ template concepts\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/NVIDIA/cutlass/blob/main/media/docs/implicit_gemm_convolution.md\"\u003eImplicit GEMM Convolution\u003c/a\u003e - describes 2-D and 3-D convolution in CUTLASS\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/NVIDIA/cutlass/blob/main/media/docs/code_organization.md\"\u003eCode Organization\u003c/a\u003e - describes the organization and contents of the CUTLASS project\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/NVIDIA/cutlass/blob/main/media/docs/terminology.md\"\u003eTerminology\u003c/a\u003e - describes terms used in the code\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/NVIDIA/cutlass/blob/main/media/docs/programming_guidelines.md\"\u003eProgramming Guidelines\u003c/a\u003e - guidelines for writing efficient modern CUDA C++\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/NVIDIA/cutlass/blob/main/media/docs/fundamental_types.md\"\u003eFundamental types\u003c/a\u003e - describes basic C++ classes used in CUTLASS to represent numeric quantities and arrays\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/NVIDIA/cutlass/blob/main/media/docs/layout.md\"\u003eLayouts\u003c/a\u003e - describes layouts of matrices and tensors in memory\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/NVIDIA/cutlass/blob/main/media/docs/tile_iterator_concept.md\"\u003eTile Iterators\u003c/a\u003e - describes C++ concepts for iterating over tiles of matrices in memory\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/NVIDIA/cutlass/blob/main/media/docs/profiler.md\"\u003eCUTLASS Profiler\u003c/a\u003e - command-line driven profiling application\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/NVIDIA/cutlass/blob/main/media/docs/utilities.md\"\u003eCUTLASS Utilities\u003c/a\u003e - additional templates used to facilitate rapid development\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/NVIDIA/cutlass/blob/main/media/docs/dependent_kernel_launch.md\"\u003eDependent kernel launch\u003c/a\u003e - describes a new feature in Hopper which allows overlapping dependent\nkernels in the same stream, and how it is used in CUTLASS.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eResources\u003c/h1\u003e\u003ca id=\"user-content-resources\" class=\"anchor\" aria-label=\"Permalink: Resources\" href=\"#resources\"\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 have also described the structure of an efficient GEMM in our talk at the\n\u003ca href=\"http://on-demand.gputechconf.com/gtc/2018/presentation/s8854-cutlass-software-primitives-for-dense-linear-algebra-at-all-levels-and-scales-within-cuda.pdf\" rel=\"nofollow\"\u003eGPU Technology Conference 2018\u003c/a\u003e.\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://www.nvidia.com/en-us/on-demand/session/gtcsiliconvalley2018-s8854/\" rel=\"nofollow\"\u003eCUTLASS: Software Primitives for Dense Linear Algebra at All Levels and Scales within CUDA\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.nvidia.com/en-us/on-demand/session/gtcsj20-s21745/\" rel=\"nofollow\"\u003eDeveloping CUDA Kernels to Push Tensor Cores to the Absolute Limit on NVIDIA A100\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.nvidia.com/en-us/on-demand/session/gtcspring21-s31883/\" rel=\"nofollow\"\u003eAccelerating Convolution with Tensor Cores in CUTLASS\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.nvidia.com/en-us/on-demand/session/gtcspring22-s41996/\" rel=\"nofollow\"\u003eAccelerating Backward Data Gradient by Increasing Tensor Core Utilization in CUTLASS\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.nvidia.com/en-us/on-demand/session/gtcfall22-a41131/\" rel=\"nofollow\"\u003eCUTLASS: Python API, Enhancements, and NVIDIA Hopper\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eBuilding CUTLASS\u003c/h1\u003e\u003ca id=\"user-content-building-cutlass\" class=\"anchor\" aria-label=\"Permalink: Building CUTLASS\" href=\"#building-cutlass\"\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\"\u003eCUTLASS is a header-only template library and does not need to be built to be used by other\nprojects. Client applications should target CUTLASS's \u003ccode\u003einclude/\u003c/code\u003e directory in their include\npaths.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eCUTLASS unit tests, examples, and utilities can be build with CMake.\nThe minimum version of CMake is given in the \u003ca href=\"/NVIDIA/cutlass/blob/main/media/docs/quickstart.md\"\u003eQuickstart guide\u003c/a\u003e.\nMake sure the \u003ccode\u003eCUDACXX\u003c/code\u003e environment variable points to NVCC in the CUDA Toolkit installed\non your system.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"$ export CUDACXX=${CUDA_INSTALL_PATH}/bin/nvcc\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e CUDACXX=\u003cspan class=\"pl-smi\"\u003e${CUDA_INSTALL_PATH}\u003c/span\u003e/bin/nvcc\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eCreate a build directory within the CUTLASS project, then run CMake. By default CUTLASS will build kernels\nfor CUDA architecture versions 5.0, 6.0, 6.1, 7.0, 7.5, 8.0, 8.6, 8.9, and 9.0.\nTo reduce compile time you can specify\nthe architectures to build CUTLASS for by changing the CMake configuration setting\n\u003ccode\u003eCUTLASS_NVCC_ARCHS\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"$ mkdir build \u0026amp;\u0026amp; cd build\n\n$ cmake .. -DCUTLASS_NVCC_ARCHS=80 # compiles for NVIDIA's Ampere Architecture\"\u003e\u003cpre\u003e$ mkdir build \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e build\n\n$ cmake .. -DCUTLASS_NVCC_ARCHS=80 \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e compiles for NVIDIA's Ampere Architecture\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eFrom the \u003ccode\u003ebuild/\u003c/code\u003e directory, compile and run the CUTLASS unit tests by building the target \u003ccode\u003etest_unit\u003c/code\u003e with make.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThe unit tests are organized as several binaries mirroring the top-level namespaces of CUTLASS,\nand they may be executed in parallel via make's \u003ccode\u003e-j\u003c/code\u003e command line argument.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"$ make test_unit -j\n...\n...\n...\n[----------] Global test environment tear-down\n[==========] 946 tests from 57 test cases ran. (10812 ms total)\n[ PASSED ] 946 tests.\"\u003e\u003cpre\u003e$ make test_unit -j\n...\n...\n...\n[----------] Global \u003cspan class=\"pl-c1\"\u003etest\u003c/span\u003e environment tear-down\n[\u003cspan class=\"pl-k\"\u003e==========\u003c/span\u003e] 946 tests from 57 \u003cspan class=\"pl-c1\"\u003etest\u003c/span\u003e cases ran. (10812 ms total)\n[ PASSED ] 946 tests.\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eAll tests should pass on supported platforms, though the exact number of tests may vary over time.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eProject Structure\u003c/h1\u003e\u003ca id=\"user-content-project-structure\" class=\"anchor\" aria-label=\"Permalink: Project Structure\" href=\"#project-structure\"\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\"\u003eCUTLASS is arranged as a header-only library along with Utilities, Tools, Examples, and unit tests.\n\u003ca href=\"https://nvidia.github.io/cutlass\" rel=\"nofollow\"\u003eDoxygen documentation\u003c/a\u003e provides a complete list of files, classes,\nand template concepts defined in the CUTLASS project.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eA detailed explanation of the source code organization may be found in the\n\u003ca href=\"/NVIDIA/cutlass/blob/main/media/docs/code_organization.md\"\u003eCUTLASS documentation\u003c/a\u003e, but several main components are summarized below.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCUTLASS Template Library\u003c/h2\u003e\u003ca id=\"user-content-cutlass-template-library\" class=\"anchor\" aria-label=\"Permalink: CUTLASS Template Library\" href=\"#cutlass-template-library\"\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=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"include/ # client applications should target this directory in their build's include paths\n\n cutlass/ # CUDA Templates for Linear Algebra Subroutines and Solvers - headers only\n\n arch/ # direct exposure of architecture features (including instruction-level GEMMs)\n\n conv/ # code specialized for convolution\n\n epilogue/ # code specialized for the epilogue of gemm/convolution\n\n gemm/ # code specialized for general matrix product computations\n\n layout/ # layout definitions for matrices, tensors, and other mathematical objects in memory\n\n platform/ # CUDA-capable Standard Library components\n\n reduction/ # bandwidth-limited reduction kernels that do not fit the \u0026quot;gemm\u0026quot; model\n\n thread/ # simt code that can be performed within a CUDA thread\n \n transform/ # code specialized for layout, type, and domain transformations\n\n * # core vocabulary types, containers, and basic numeric operations\n\n cute/ # CuTe Layout, layout algebra, MMA/Copy atoms, tiled MMA/Copy\n\n algorithm/ # Definitions of core operations such as copy, gemm, and operations on cute::tuples\n\n arch/ # Bare bones PTX wrapper structs for copy and math instructions\n\n atom/ # Meta-information either link to or built from arch/ operators\n\n mma_atom.hpp # cute::Mma_Atom and cute::TiledMma\n\n copy_atom.hpp # cute::Copy_Atom and cute::TiledCopy\n\n *sm*.hpp # Arch specific meta-information for copy and math operations\n\n * # Core library types such as Shape, Stride, Layout, Tensor, and associated operations\n\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003einclude/ # client applications should target this directory in their build's include paths\n\n cutlass/ # CUDA Templates for Linear Algebra Subroutines and Solvers - headers only\n\n arch/ # direct exposure of architecture features (including instruction-level GEMMs)\n\n conv/ # code specialized for convolution\n\n epilogue/ # code specialized for the epilogue of gemm/convolution\n\n gemm/ # code specialized for general matrix product computations\n\n layout/ # layout definitions for matrices, tensors, and other mathematical objects in memory\n\n platform/ # CUDA-capable Standard Library components\n\n reduction/ # bandwidth-limited reduction kernels that do not fit the \"gemm\" model\n\n thread/ # simt code that can be performed within a CUDA thread\n \n transform/ # code specialized for layout, type, and domain transformations\n\n * # core vocabulary types, containers, and basic numeric operations\n\n cute/ # CuTe Layout, layout algebra, MMA/Copy atoms, tiled MMA/Copy\n\n algorithm/ # Definitions of core operations such as copy, gemm, and operations on cute::tuples\n\n arch/ # Bare bones PTX wrapper structs for copy and math instructions\n\n atom/ # Meta-information either link to or built from arch/ operators\n\n mma_atom.hpp # cute::Mma_Atom and cute::TiledMma\n\n copy_atom.hpp # cute::Copy_Atom and cute::TiledCopy\n\n *sm*.hpp # Arch specific meta-information for copy and math operations\n\n * # Core library types such as Shape, Stride, Layout, Tensor, and associated operations\n\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCUTLASS SDK Examples\u003c/h3\u003e\u003ca id=\"user-content-cutlass-sdk-examples\" class=\"anchor\" aria-label=\"Permalink: CUTLASS SDK Examples\" href=\"#cutlass-sdk-examples\"\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\u003ca href=\"/NVIDIA/cutlass/blob/main/examples\"\u003eCUTLASS SDK examples\u003c/a\u003e apply CUTLASS templates to implement basic computations.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eTools\u003c/h3\u003e\u003ca id=\"user-content-tools\" class=\"anchor\" aria-label=\"Permalink: Tools\" href=\"#tools\"\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=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"tools/\n library/ # CUTLASS Instance Library - contains instantiations of all supported CUTLASS templates\n include/\n cutlass/\n library/\n\n profiler/ # CUTLASS Profiler - command-line utility for executing operations in the\n # CUTLASS Library\n \n util/ # CUTLASS Utilities - contains numerous helper classes for\n include/ # manging tensors in device memory, reference\n cutlass/ # implementations for GEMM, random initialization\n util/ # of tensors, and I/O.\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003etools/\n library/ # CUTLASS Instance Library - contains instantiations of all supported CUTLASS templates\n include/\n cutlass/\n library/\n\n profiler/ # CUTLASS Profiler - command-line utility for executing operations in the\n # CUTLASS Library\n \n util/ # CUTLASS Utilities - contains numerous helper classes for\n include/ # manging tensors in device memory, reference\n cutlass/ # implementations for GEMM, random initialization\n util/ # of tensors, and I/O.\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eTest\u003c/h3\u003e\u003ca id=\"user-content-test\" class=\"anchor\" aria-label=\"Permalink: Test\" href=\"#test\"\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 \u003ccode\u003etest/unit/\u003c/code\u003e directory consist of unit tests implemented with Google Test that demonstrate\nbasic usage of Core API components and complete tests of the CUTLASS GEMM computations.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eInstructions for building and running the Unit tests are described in the \u003ca href=\"/NVIDIA/cutlass/blob/main/media/docs/quickstart.md\"\u003eQuickstart guide\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003ePerformance Profiling\u003c/h1\u003e\u003ca id=\"user-content-performance-profiling\" class=\"anchor\" aria-label=\"Permalink: Performance Profiling\" href=\"#performance-profiling\"\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 \u003ccode\u003etools/profiler/\u003c/code\u003e directory contains a command-line utility for launching each of the GEMM kernels.\nIt can be built as follows:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"$ make cutlass_profiler -j16\"\u003e\u003cpre\u003e$ make cutlass_profiler -j16\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eBuilding all GEMM and Convolution kernels (\u003cem\u003elong\u003c/em\u003e build times)\u003c/h2\u003e\u003ca id=\"user-content-building-all-gemm-and-convolution-kernels-long-build-times\" class=\"anchor\" aria-label=\"Permalink: Building all GEMM and Convolution kernels (long build times)\" href=\"#building-all-gemm-and-convolution-kernels-long-build-times\"\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\"\u003eBy default, only one tile size is instantiated for each data type, math instruction, and layout.\nTo instantiate all, set the following environment variable when running CMake from an empty \u003ccode\u003ebuild/\u003c/code\u003e directory.\nBeware, this results in \u003cem\u003etens of thousands\u003c/em\u003e of kernels and long build times.\nThis would also result in a large binary size and on some platforms linker to fail on building the library.\nTherefore, it's highly recommended to generate only a subset of kernels as demonstrated in the sub-section below.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"$ cmake .. -DCUTLASS_NVCC_ARCHS=90a -DCUTLASS_LIBRARY_KERNELS=all\n...\n$ make cutlass_profiler -j16\"\u003e\u003cpre\u003e$ cmake .. -DCUTLASS_NVCC_ARCHS=90a -DCUTLASS_LIBRARY_KERNELS=all\n...\n$ make cutlass_profiler -j16\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eBuilding a subset of GEMM and Convolution kernels (\u003cem\u003ereduced\u003c/em\u003e build times)\u003c/h2\u003e\u003ca id=\"user-content-building-a-subset-of-gemm-and-convolution-kernels-reduced-build-times\" class=\"anchor\" aria-label=\"Permalink: Building a subset of GEMM and Convolution kernels (reduced build times)\" href=\"#building-a-subset-of-gemm-and-convolution-kernels-reduced-build-times\"\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\"\u003eTo compile strictly one kernel or a small set of kernels, a comma-delimited list of kernel names with\nwildcard characters may be used to reduce the set of kernels. The following examples show building exactly one\nor a subset of kernels for NVIDIA Ampere and Turing architecture:\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eBuilding a subset Tensor Core GEMM kernels\u003c/h3\u003e\u003ca id=\"user-content-building-a-subset-tensor-core-gemm-kernels\" class=\"anchor\" aria-label=\"Permalink: Building a subset Tensor Core GEMM kernels\" href=\"#building-a-subset-tensor-core-gemm-kernels\"\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\"\u003eTo compile a subset of Tensor Core GEMM kernels with FP32 accumulation and FP16 input targeting NVIDIA Ampere and Turing architecture,\nuse the below cmake command line:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"$ cmake .. -DCUTLASS_NVCC_ARCHS='75;80' -DCUTLASS_LIBRARY_KERNELS=cutlass_tensorop_s*gemm_f16_*_nt_align8\n...\n$ make cutlass_profiler -j16\"\u003e\u003cpre\u003e$ cmake .. -DCUTLASS_NVCC_ARCHS=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003e75;80\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003e\u003c/span\u003e -DCUTLASS_LIBRARY_KERNELS=cutlass_tensorop_s\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003egemm_f16_\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e_nt_align8\n...\n$ make cutlass_profiler -j16\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eExample command line for profiling a subset of Tensor Core GEMM kernels is as follows:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"./tools/profiler/cutlass_profiler --kernels=cutlass_tensorop_s*gemm_f16_*_nt_align8 --m=3456 --n=4096 --k=4096\n\n...\n=============================\n Problem ID: 1\n\n Provider: CUTLASS\n OperationKind: gemm\n Operation: cutlass_tensorop_s1688gemm_f16_256x128_32x2_nt_align8\n\n Status: Success\n Verification: ON\n Disposition: Passed\n\nreference_device: Passed\n cuBLAS: Passed\n\n Arguments: --gemm_kind=universal --m=3456 --n=4096 --k=4096 --A=f16:column --B=f16:row --C=f32:column --alpha=1 \\\n --beta=0 --split_k_slices=1 --batch_count=1 --op_class=tensorop --accum=f32 --cta_m=256 --cta_n=128 \\\n --cta_k=32 --stages=2 --warps_m=4 --warps_n=2 --warps_k=1 --inst_m=16 --inst_n=8 --inst_k=8 --min_cc=75 \\\n --max_cc=1024\n\n Bytes: 118489088 bytes\n FLOPs: 115992428544 flops\n\n Runtime: 1.55948 ms\n Memory: 70.7616 GiB/s\n\n Math: 74378.8 GFLOP/s\n\n\n\n=============================\n...\"\u003e\u003cpre\u003e./tools/profiler/cutlass_profiler --kernels=cutlass_tensorop_s\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003egemm_f16_\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e_nt_align8 --m=3456 --n=4096 --k=4096\n\n...\n=============================\n Problem ID: 1\n\n Provider: CUTLASS\n OperationKind: gemm\n Operation: cutlass_tensorop_s1688gemm_f16_256x128_32x2_nt_align8\n\n Status: Success\n Verification: ON\n Disposition: Passed\n\nreference_device: Passed\n cuBLAS: Passed\n\n Arguments: --gemm_kind=universal --m=3456 --n=4096 --k=4096 --A=f16:column --B=f16:row --C=f32:column --alpha=1 \\\n --beta=0 --split_k_slices=1 --batch_count=1 --op_class=tensorop --accum=f32 --cta_m=256 --cta_n=128 \\\n --cta_k=32 --stages=2 --warps_m=4 --warps_n=2 --warps_k=1 --inst_m=16 --inst_n=8 --inst_k=8 --min_cc=75 \\\n --max_cc=1024\n\n Bytes: 118489088 bytes\n FLOPs: 115992428544 flops\n\n Runtime: 1.55948 ms\n Memory: 70.7616 GiB/s\n\n Math: 74378.8 GFLOP/s\n\n\n\n=============================\n...\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eBuilding one CUDA Core GEMM kernel\u003c/h3\u003e\u003ca id=\"user-content-building-one-cuda-core-gemm-kernel\" class=\"anchor\" aria-label=\"Permalink: Building one CUDA Core GEMM kernel\" href=\"#building-one-cuda-core-gemm-kernel\"\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\"\u003eTo compile one SGEMM kernel targeting NVIDIA Ampere and Turing architecture, use the below cmake command line:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"$ cmake .. -DCUTLASS_NVCC_ARCHS='75;80' -DCUTLASS_LIBRARY_KERNELS=cutlass_simt_sgemm_128x128_8x2_nn_align1\n...\n$ make cutlass_profiler -j16\"\u003e\u003cpre\u003e$ cmake .. -DCUTLASS_NVCC_ARCHS=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003e75;80\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003e\u003c/span\u003e -DCUTLASS_LIBRARY_KERNELS=cutlass_simt_sgemm_128x128_8x2_nn_align1\n...\n$ make cutlass_profiler -j16\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eExample command line for profiling single SGEMM CUDA kernel is as follows:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"$ ./tools/profiler/cutlass_profiler --kernels=sgemm --m=3456 --n=4096 --k=4096\n\n=============================\n Problem ID: 1\n\n Provider: CUTLASS\n OperationKind: gemm\n Operation: cutlass_simt_sgemm_128x128_8x2_nn_align1\n\n Status: Success\n Verification: ON\n Disposition: Passed\n\n cuBLAS: Passed\n\n Arguments: --m=3456 --n=4096 --k=4096 --A=f32:column --B=f32:column --C=f32:column --alpha=1 --beta=0 --split_k_slices=1 \\\n --batch_count=1 --op_class=simt --accum=f32 --cta_m=128 --cta_n=128 --cta_k=8 --stages=2 --warps_m=4 \\\n --warps_n=2 --warps_k=1 --inst_m=1 --inst_n=1 --inst_k=1 --min_cc=50 --max_cc=1024\n\n Bytes: 180355072 bytes\n FLOPs: 115992428544 flops\n\n Runtime: 6.73655 ms\n Memory: 24.934 GiB/s\n\n Math: 17218.4 GFLOP/s\n\n=============================\"\u003e\u003cpre\u003e$ ./tools/profiler/cutlass_profiler --kernels=sgemm --m=3456 --n=4096 --k=4096\n\n=============================\n Problem ID: 1\n\n Provider: CUTLASS\n OperationKind: gemm\n Operation: cutlass_simt_sgemm_128x128_8x2_nn_align1\n\n Status: Success\n Verification: ON\n Disposition: Passed\n\n cuBLAS: Passed\n\n Arguments: --m=3456 --n=4096 --k=4096 --A=f32:column --B=f32:column --C=f32:column --alpha=1 --beta=0 --split_k_slices=1 \\\n --batch_count=1 --op_class=simt --accum=f32 --cta_m=128 --cta_n=128 --cta_k=8 --stages=2 --warps_m=4 \\\n --warps_n=2 --warps_k=1 --inst_m=1 --inst_n=1 --inst_k=1 --min_cc=50 --max_cc=1024\n\n Bytes: 180355072 bytes\n FLOPs: 115992428544 flops\n\n Runtime: 6.73655 ms\n Memory: 24.934 GiB/s\n\n Math: 17218.4 GFLOP/s\n\n=============================\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eBuilding a subset of Tensor Core Convolution kernels\u003c/h3\u003e\u003ca id=\"user-content-building-a-subset-of-tensor-core-convolution-kernels\" class=\"anchor\" aria-label=\"Permalink: Building a subset of Tensor Core Convolution kernels\" href=\"#building-a-subset-of-tensor-core-convolution-kernels\"\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\"\u003eTo compile a subset of Tensor core convolution kernels implementing forward propagation (fprop) with FP32 accumulation\nand FP16 input targeting NVIDIA Ampere and Turing architecture, use the below cmake command line:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"$ cmake .. -DCUTLASS_NVCC_ARCHS='75;80' -DCUTLASS_LIBRARY_KERNELS=cutlass_tensorop_s*fprop_optimized_f16\n...\n$ make cutlass_profiler -j16\"\u003e\u003cpre\u003e$ cmake .. -DCUTLASS_NVCC_ARCHS=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003e75;80\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003e\u003c/span\u003e -DCUTLASS_LIBRARY_KERNELS=cutlass_tensorop_s\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003efprop_optimized_f16\n...\n$ make cutlass_profiler -j16\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eExample command line for profiling a subset of Tensor Core convolution kernels is as follows:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"$ ./tools/profiler/cutlass_profiler --kernels=cutlass_tensorop_s*fprop_optimized_f16 --n=8 --h=224 --w=224 --c=128 --k=128 --r=3 --s=3\n\n...\n=============================\n Problem ID: 1\n\n Provider: CUTLASS\n OperationKind: conv2d\n Operation: cutlass_tensorop_s16816fprop_optimized_f16_128x128_32x5_nhwc\n\n Status: Success\n Verification: ON\n Disposition: Passed\n\nreference_device: Passed\n\n Arguments: --conv_kind=fprop --n=8 --h=224 --w=224 --c=128 --k=128 --r=3 --s=3 --p=224 --q=224 --pad_h=1 --pad_w=1 \\\n --stride_h=1 --stride_w=1 --dilation_h=1 --dilation_w=1 --Activation=f16:nhwc --Filter=f16:nhwc --Output=f32:nhwc \\\n --conv_mode=cross --iterator_algorithm=optimized --alpha=1 --beta=0 --split_k_mode=serial --split_k_slices=1 \\\n --eq_gemm_provider=none --op_class=tensorop --accum=f32 --cta_m=128 --cta_n=128 --cta_k=32 --stages=5 \\\n --warps_m=2 --warps_n=2 --warps_k=1 --inst_m=16 --inst_n=8 --inst_k=16 --min_cc=80 --max_cc=1024\n\n Bytes: 1130659840 bytes\n FLOPs: 118482796544 flops\n\n Runtime: 0.711496 ms\n Memory: 1479.99 GiB/s\n\n Math: 166526 GFLOP/s\n\n=============================\n...\"\u003e\u003cpre\u003e$ ./tools/profiler/cutlass_profiler --kernels=cutlass_tensorop_s\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003efprop_optimized_f16 --n=8 --h=224 --w=224 --c=128 --k=128 --r=3 --s=3\n\n...\n=============================\n Problem ID: 1\n\n Provider: CUTLASS\n OperationKind: conv2d\n Operation: cutlass_tensorop_s16816fprop_optimized_f16_128x128_32x5_nhwc\n\n Status: Success\n Verification: ON\n Disposition: Passed\n\nreference_device: Passed\n\n Arguments: --conv_kind=fprop --n=8 --h=224 --w=224 --c=128 --k=128 --r=3 --s=3 --p=224 --q=224 --pad_h=1 --pad_w=1 \\\n --stride_h=1 --stride_w=1 --dilation_h=1 --dilation_w=1 --Activation=f16:nhwc --Filter=f16:nhwc --Output=f32:nhwc \\\n --conv_mode=cross --iterator_algorithm=optimized --alpha=1 --beta=0 --split_k_mode=serial --split_k_slices=1 \\\n --eq_gemm_provider=none --op_class=tensorop --accum=f32 --cta_m=128 --cta_n=128 --cta_k=32 --stages=5 \\\n --warps_m=2 --warps_n=2 --warps_k=1 --inst_m=16 --inst_n=8 --inst_k=16 --min_cc=80 --max_cc=1024\n\n Bytes: 1130659840 bytes\n FLOPs: 118482796544 flops\n\n Runtime: 0.711496 ms\n Memory: 1479.99 GiB/s\n\n Math: 166526 GFLOP/s\n\n=============================\n...\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eBuilding one Convolution CUDA kernel\u003c/h3\u003e\u003ca id=\"user-content-building-one-convolution-cuda-kernel\" class=\"anchor\" aria-label=\"Permalink: Building one Convolution CUDA kernel\" href=\"#building-one-convolution-cuda-kernel\"\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\"\u003eTo compile and run one CUDA Core convolution kernel implementing forward propagation (fprop) with F32 accumulation\nand FP32 input targeting NVIDIA Ampere and Turing architecture, use the below cmake command line:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"$ cmake .. -DCUTLASS_NVCC_ARCHS='75;80' -DCUTLASS_LIBRARY_KERNELS=cutlass_simt_sfprop_optimized_128x128_8x2_nhwc\n...\n$ make cutlass_profiler -j16\"\u003e\u003cpre\u003e$ cmake .. -DCUTLASS_NVCC_ARCHS=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003e75;80\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003e\u003c/span\u003e -DCUTLASS_LIBRARY_KERNELS=cutlass_simt_sfprop_optimized_128x128_8x2_nhwc\n...\n$ make cutlass_profiler -j16\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eExample command line for profiling one CUDA Core convolution kernel:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"$ ./tools/profiler/cutlass_profiler --kernels=cutlass_simt_sfprop_optimized_128x128_8x2_nhwc --n=8 --h=224 --w=224 --c=128 --k=128 --r=3 --s=3\n\n\n=============================\n Problem ID: 1\n\n Provider: CUTLASS\n OperationKind: conv2d\n Operation: cutlass_simt_sfprop_optimized_128x128_8x2_nhwc\n\n Status: Success\n Verification: ON\n Disposition: Passed\n\nreference_device: Passed\n\n Arguments: --conv_kind=fprop --n=8 --h=224 --w=224 --c=128 --k=128 --r=3 --s=3 --p=224 --q=224 --pad_h=1 --pad_w=1 \\\n --stride_h=1 --stride_w=1 --dilation_h=1 --dilation_w=1 --Activation=f32:nhwc --Filter=f32:nhwc --Output=f32:nhwc \\\n --conv_mode=cross --iterator_algorithm=optimized --alpha=1 --beta=0 --split_k_mode=serial --split_k_slices=1 \\\n --eq_gemm_provider=none --op_class=simt --accum=f32 --cta_m=128 --cta_n=128 --cta_k=8 --stages=2 --warps_m=4 \\\n --warps_n=2 --warps_k=1 --inst_m=1 --inst_n=1 --inst_k=1 --min_cc=50 --max_cc=1024\n\n Bytes: 2055798784 bytes\n FLOPs: 118482796544 flops\n\n Runtime: 7.34266 ms\n Memory: 260.752 GiB/s\n\n Math: 16136.2 GFLOP/s\n\n\n=============================\n\"\u003e\u003cpre\u003e$ ./tools/profiler/cutlass_profiler --kernels=cutlass_simt_sfprop_optimized_128x128_8x2_nhwc --n=8 --h=224 --w=224 --c=128 --k=128 --r=3 --s=3\n\n\n=============================\n Problem ID: 1\n\n Provider: CUTLASS\n OperationKind: conv2d\n Operation: cutlass_simt_sfprop_optimized_128x128_8x2_nhwc\n\n Status: Success\n Verification: ON\n Disposition: Passed\n\nreference_device: Passed\n\n Arguments: --conv_kind=fprop --n=8 --h=224 --w=224 --c=128 --k=128 --r=3 --s=3 --p=224 --q=224 --pad_h=1 --pad_w=1 \\\n --stride_h=1 --stride_w=1 --dilation_h=1 --dilation_w=1 --Activation=f32:nhwc --Filter=f32:nhwc --Output=f32:nhwc \\\n --conv_mode=cross --iterator_algorithm=optimized --alpha=1 --beta=0 --split_k_mode=serial --split_k_slices=1 \\\n --eq_gemm_provider=none --op_class=simt --accum=f32 --cta_m=128 --cta_n=128 --cta_k=8 --stages=2 --warps_m=4 \\\n --warps_n=2 --warps_k=1 --inst_m=1 --inst_n=1 --inst_k=1 --min_cc=50 --max_cc=1024\n\n Bytes: 2055798784 bytes\n FLOPs: 118482796544 flops\n\n Runtime: 7.34266 ms\n Memory: 260.752 GiB/s\n\n Math: 16136.2 GFLOP/s\n\n\n=============================\n\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eMore Details on Compiling CUTLASS Kernels and CUTLASS Profiler\u003c/h2\u003e\u003ca id=\"user-content-more-details-on-compiling-cutlass-kernels-and-cutlass-profiler\" class=\"anchor\" aria-label=\"Permalink: More Details on Compiling CUTLASS Kernels and CUTLASS Profiler\" href=\"#more-details-on-compiling-cutlass-kernels-and-cutlass-profiler\"\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\u003ePlease follow the links for more CMake examples on selectively compiling CUTLASS kernels:\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"/NVIDIA/cutlass/blob/main/media/docs/quickstart.md#gemm-cmake-examples\"\u003eGEMM CMake Examples\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/NVIDIA/cutlass/blob/main/media/docs/quickstart.md#convolution-cmake-examples\"\u003eImplicit GEMM convolution CMake Examples\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/NVIDIA/cutlass/blob/main/media/docs/profiler.md\"\u003eFurther details about the CUTLASS Profiler are described here.\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eAbout\u003c/h1\u003e\u003ca id=\"user-content-about\" class=\"anchor\" aria-label=\"Permalink: About\" href=\"#about\"\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\"\u003eCUTLASS is released by NVIDIA Corporation as Open Source software under the\n\u003ca href=\"/NVIDIA/cutlass/blob/main/LICENSE.txt\"\u003e3-clause \"New\" BSD license\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eContributors\u003c/h1\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\"\u003eThe official list of CUTLASS developers and contributors is available here: \u003ca href=\"/NVIDIA/cutlass/blob/main/CONTRIBUTORS.md\"\u003eCONTRIBUTORS\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCopyright\u003c/h1\u003e\u003ca id=\"user-content-copyright\" class=\"anchor\" aria-label=\"Permalink: Copyright\" href=\"#copyright\"\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\"\u003eCopyright (c) 2017 - 2025 NVIDIA CORPORATION \u0026amp; AFFILIATES. All rights reserved.\nSPDX-License-Identifier: BSD-3-Clause\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\" Redistribution and use in source and binary forms, with or without\n modification, are permitted provided that the following conditions are met:\n\n 1. Redistributions of source code must retain the above copyright notice, this\n list of conditions and the following disclaimer.\n\n 2. Redistributions in binary form must reproduce the above copyright notice,\n this list of conditions and the following disclaimer in the documentation\n and/or other materials provided with the distribution.\n\n 3. 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IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE\n FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL\n DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR\n SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER\n CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,\n OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE\n OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003c/article\u003e","loaded":true,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":[{"level":1,"text":"CUTLASS 3.8.0","anchor":"cutlass-380","htmlText":"CUTLASS 3.8.0"},{"level":1,"text":"What's New in CUTLASS 3.8","anchor":"whats-new-in-cutlass-38","htmlText":"What's New in CUTLASS 3.8"},{"level":1,"text":"Performance","anchor":"performance","htmlText":"Performance"},{"level":1,"text":"CuTe","anchor":"cute","htmlText":"CuTe"},{"level":1,"text":"Compatibility","anchor":"compatibility","htmlText":"Compatibility"},{"level":2,"text":"Operating Systems","anchor":"operating-systems","htmlText":"Operating Systems"},{"level":2,"text":"Hardware","anchor":"hardware","htmlText":"Hardware"},{"level":2,"text":"Target Architecture","anchor":"target-architecture","htmlText":"Target Architecture"},{"level":1,"text":"Documentation","anchor":"documentation","htmlText":"Documentation"},{"level":1,"text":"Resources","anchor":"resources","htmlText":"Resources"},{"level":1,"text":"Building CUTLASS","anchor":"building-cutlass","htmlText":"Building CUTLASS"},{"level":1,"text":"Project Structure","anchor":"project-structure","htmlText":"Project Structure"},{"level":2,"text":"CUTLASS Template Library","anchor":"cutlass-template-library","htmlText":"CUTLASS Template Library"},{"level":3,"text":"CUTLASS SDK Examples","anchor":"cutlass-sdk-examples","htmlText":"CUTLASS SDK Examples"},{"level":3,"text":"Tools","anchor":"tools","htmlText":"Tools"},{"level":3,"text":"Test","anchor":"test","htmlText":"Test"},{"level":1,"text":"Performance Profiling","anchor":"performance-profiling","htmlText":"Performance Profiling"},{"level":2,"text":"Building all GEMM and Convolution kernels (long build times)","anchor":"building-all-gemm-and-convolution-kernels-long-build-times","htmlText":"Building all GEMM and Convolution kernels (long build times)"},{"level":2,"text":"Building a subset of GEMM and Convolution kernels (reduced build times)","anchor":"building-a-subset-of-gemm-and-convolution-kernels-reduced-build-times","htmlText":"Building a subset of GEMM and Convolution kernels (reduced build times)"},{"level":3,"text":"Building a subset Tensor Core GEMM kernels","anchor":"building-a-subset-tensor-core-gemm-kernels","htmlText":"Building a subset Tensor Core GEMM kernels"},{"level":3,"text":"Building one CUDA Core GEMM kernel","anchor":"building-one-cuda-core-gemm-kernel","htmlText":"Building one CUDA Core GEMM kernel"},{"level":3,"text":"Building a subset of Tensor Core Convolution kernels","anchor":"building-a-subset-of-tensor-core-convolution-kernels","htmlText":"Building a subset of Tensor Core Convolution kernels"},{"level":3,"text":"Building one Convolution CUDA kernel","anchor":"building-one-convolution-cuda-kernel","htmlText":"Building one Convolution CUDA kernel"},{"level":2,"text":"More Details on Compiling CUTLASS Kernels and CUTLASS Profiler","anchor":"more-details-on-compiling-cutlass-kernels-and-cutlass-profiler","htmlText":"More Details on Compiling CUTLASS Kernels and CUTLASS Profiler"},{"level":1,"text":"About","anchor":"about","htmlText":"About"},{"level":1,"text":"Contributors","anchor":"contributors","htmlText":"Contributors"},{"level":1,"text":"Copyright","anchor":"copyright","htmlText":"Copyright"}],"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2FNVIDIA%2Fcutlass"}},{"displayName":"LICENSE.txt","repoName":"cutlass","refName":"main","path":"LICENSE.txt","preferredFileType":"license","tabName":"License","richText":null,"loaded":false,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":null,"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2FNVIDIA%2Fcutlass"}}],"overviewFilesProcessingTime":0}},"appPayload":{"helpUrl":"https://docs.github.com","findFileWorkerPath":"/assets-cdn/worker/find-file-worker-9f8a877aa99f.js","findInFileWorkerPath":"/assets-cdn/worker/find-in-file-worker-96e76d5fdb2c.js","githubDevUrl":null,"enabled_features":{"copilot_workspace":null,"code_nav_ui_events":false,"overview_shared_code_dropdown_button":false,"react_blob_overlay":false,"copilot_conversational_ux_embedding_update":false,"copilot_smell_icebreaker_ux":true,"accessible_code_button":true}}}}</script> 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1 1-1.6 5.685.75.75 0 1 1 1.493-.154 6.5 6.5 0 1 0 1.18-4.458l1.358 1.358A.25.25 0 0 1 3.896 6H.25A.25.25 0 0 1 0 5.75V2.104a.25.25 0 0 1 .427-.177ZM7.75 4a.75.75 0 0 1 .75.75v2.992l2.028.812a.75.75 0 0 1-.557 1.392l-2.5-1A.751.751 0 0 1 7 8.25v-3.5A.75.75 0 0 1 7.75 4Z"></path></svg></span></span></a></span></div></div></div></div></td></tr><tr class="react-directory-row undefined" id="folder-row-0"><td class="react-directory-row-name-cell-small-screen" colSpan="2"><div class="react-directory-filename-column"><svg aria-hidden="true" focusable="false" class="icon-directory" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" style="display:inline-block;user-select:none;vertical-align:text-bottom;overflow:visible"><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 class="overflow-hidden"><div 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itemprop="text"><p dir="auto"><a target="_blank" rel="noopener noreferrer" href="/NVIDIA/cutlass/blob/main/media/images/gemm-hierarchy-with-epilogue-no-labels.png"><img src="/NVIDIA/cutlass/raw/main/media/images/gemm-hierarchy-with-epilogue-no-labels.png" alt="ALT" title="Complete CUDA GEMM decomposition" style="max-width: 100%;"></a></p> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">CUTLASS 3.8.0</h1><a id="user-content-cutlass-380" class="anchor" aria-label="Permalink: CUTLASS 3.8.0" href="#cutlass-380"><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"><em>CUTLASS 3.8.0 - January 2025</em></p> <p dir="auto">CUTLASS is a collection of CUDA C++ template abstractions for implementing high-performance matrix-matrix multiplication (GEMM) and related computations at all levels and scales within CUDA. It incorporates strategies for hierarchical decomposition and data movement similar to those used to implement cuBLAS and cuDNN. CUTLASS decomposes these "moving parts" into reusable, modular software components abstracted by C++ template classes. Primitives for different levels of a conceptual parallelization hierarchy can be specialized and tuned via custom tiling sizes, data types, and other algorithmic policy. The resulting flexibility simplifies their use as building blocks within custom kernels and applications.</p> <p dir="auto">To support a wide variety of applications, CUTLASS provides extensive support for mixed-precision computations, providing specialized data-movement and multiply-accumulate abstractions for FP64, FP32, TF32, FP16, BF16, <a href="/NVIDIA/cutlass/blob/main/examples/27_ampere_3xtf32_fast_accurate_tensorop_gemm">FP32 emulation via tensor core instruction</a>, 8b floating point types (e5m2 and e4m3), block scaled data types (NVIDIA NVFP4 and OCP standard MXFP4, MXFP6, MXFP8), narrow integer types (4 and 8b signed and unsigned integers), and binary 1b data types (where architectures allow for the native support of such data types). CUTLASS demonstrates optimal matrix multiply operations targeting the programmable, high-throughput <em>Tensor Cores</em> implemented by NVIDIA's Volta, Turing, Ampere, Ada, Hopper, and Blackwell architectures.</p> <p dir="auto">In addition to GEMMs, CUTLASS implements high-performance convolution via the implicit GEMM algorithm. Implicit GEMM is the formulation of a convolution operation as a GEMM thereby taking advantage of CUTLASS's modular GEMM pipeline. This allows CUTLASS to build convolutions by reusing highly-optimized GEMM components.</p> <p dir="auto">See the <a href="/NVIDIA/cutlass/blob/main/media/docs/quickstart.md">Quick Start Guide</a> to get started quickly.</p> <p dir="auto">See the <a href="/NVIDIA/cutlass/blob/main/media/docs/functionality.md">functionality docs</a> for a more comprehensive list of kernel level features, data types, instructions, and minimum supported by CUTLASS on each GPU architecture.</p> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">What's New in CUTLASS 3.8</h1><a id="user-content-whats-new-in-cutlass-38" class="anchor" aria-label="Permalink: What's New in CUTLASS 3.8" href="#whats-new-in-cutlass-38"><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">CUTLASS 3.8 is the first release that supports the NVIDIA Blackwell SM100 architecture. For a background on Blackwell's new features, please consult the PTX documentation for CUDA 12.8.</p> <ul dir="auto"> <li>Support for new CuTe building blocks specifically for Blackwell architecture: <ul dir="auto"> <li><a href="/NVIDIA/cutlass/blob/main/include/cute/atom/mma_traits_sm100.hpp">5th generation Blackwell Tensor Core instructions (TCGen05)</a> via CuTe MMA atoms.</li> <li>Extensions to <a href="/NVIDIA/cutlass/blob/main/include/cute/atom/copy_traits_sm100_tma.hpp">Tensor Memory Accelerator</a> via CuTe Copy atoms.</li> <li>Exposure of Blackwell's new tensor memory (note: distinct from TMA) as <a href="/NVIDIA/cutlass/blob/main/include/cute/pointer.hpp#L290"><code>tmem</code></a> across CuTe as a first class data locale.</li> <li>Exposure of <a href="/NVIDIA/cutlass/blob/main/include/cute/atom/copy_traits_sm100.hpp"><code>tmem->rmem</code>, <code>rmem->tmem</code> and <code>smem->tmem data movement instructions</code></a> as copy atoms in CuTe.</li> <li><a href="/NVIDIA/cutlass/blob/main/include/cute/atom/copy_traits_sm100.hpp"><code>make_tmem_copy()</code></a> utility method to ease creation of tiled copies for tmem copy atoms.</li> <li>Support for <a href="/NVIDIA/cutlass/blob/main/include/cute/atom/copy_traits_sm100.hpp">new variants of LDSM on Blackwell</a> via CuTe Copy atoms.</li> </ul> </li> <li>Support for new CUTLASS building blocks specifically for Blackwell architecture: <ul dir="auto"> <li>Various narrow precision <a href="/NVIDIA/cutlass/blob/main/include/cutlass/exmy_base.h">FP4, FP6, and FP8</a> formats as well as their <a href="/NVIDIA/cutlass/blob/main/include/cutlass/float_subbyte.h">block-scaled variants NVFP4, MXFP4, MXFP6, and MXFP8</a></li> <li><a href="/NVIDIA/cutlass/blob/main/include/cutlass/pipeline/sm100_pipeline.hpp">Pipelines that implement Blackwell specific synchronization</a>.</li> <li><a href="/NVIDIA/cutlass/blob/main/include/cutlass/cluster_launch.hpp">Cluster launch control API supporting preferred and fallback cluster shapes</a>.</li> <li>Data types including NVFP4, MXFP4, MXFP6, and MXFP8 and all their supported element and scale factor types.</li> <li>Tile schedulers using <a href="/NVIDIA/cutlass/blob/main/media/docs/blackwell_cluster_launch_control.md">Blackwell's Cluster Launch Control (CLC) feature</a> to implement dynamic persistence scheduling for <a href="/NVIDIA/cutlass/blob/main/include/cutlass/gemm/kernel/sm100_tile_scheduler.hpp">GEMMs</a>, and <a href="/NVIDIA/cutlass/blob/main/include/cutlass/gemm/kernel/sm100_tile_scheduler_stream_k.hpp">stream-K</a>.</li> <li>Extensions to testbeds and reference check code for unit tests and CUTLASS profiler.</li> </ul> </li> <li>Full support for Blackwell kernels in CUTLASS 3.x API: <ul dir="auto"> <li><a href="/NVIDIA/cutlass/blob/main/include/cutlass/gemm/kernel/sm100_gemm_tma_warpspecialized.hpp">Blackwell specific kernel layers</a> that <ul dir="auto"> <li>Implement a new warp-specialization recipe tuned specifically for Blackwell.</li> <li>Leverage all the new features such as CLC based tile scheduling, preferred cluster, and TMEM based double buffering of accumulators.</li> <li>Support stream-K load balancing for all kernel types everywhere via composable scheduler support.</li> </ul> </li> <li>Blackwell collective mainloops that target the TCGen05 MMA instructions (both SS and TS) for <ul dir="auto"> <li><a href="/NVIDIA/cutlass/blob/main/include/cutlass/gemm/collective/sm100_mma_warpspecialized.hpp">Non-block scaled data types without support for pointer array and grouped GEMM with TMA</a></li> <li><a href="/NVIDIA/cutlass/blob/main/include/cutlass/gemm/collective/sm100_mma_array_warpspecialized.hpp">Non-block scaled data types with support for pointer array and grouped GEMM with TMA</a></li> <li><a href="/NVIDIA/cutlass/blob/main/include/cutlass/gemm/collective/sm100_blockscaled_mma_warpspecialized.hpp">Block scaled data types without support for pointer array and grouped GEMM with TMA</a></li> <li><a href="/NVIDIA/cutlass/blob/main/include/cutlass/gemm/collective/sm100_blockscaled_mma_array_warpspecialized.hpp">Block scaled data types with support for pointer array and grouped GEMM with TMA</a></li> </ul> </li> <li>Blackwell <a href="/NVIDIA/cutlass/blob/main/include/cutlass/conv/collective/sm100_implicit_gemm_umma_warpspecialized.hpp">collective mainloop for convolution kernels</a> supporting non-block scaled data types for fprop, dgrad, and wgrad.</li> <li>New <a href="/NVIDIA/cutlass/blob/main/include/cutlass/gemm/dispatch_policy.hpp">GEMM</a>, <a href="/NVIDIA/cutlass/blob/main/include/cutlass/conv/dispatch_policy.hpp">convolution</a>, and <a href="/NVIDIA/cutlass/blob/main/include/cutlass/epilogue/dispatch_policy.hpp">epilogue</a> dispatch policies for collectives, kernel layers, and builders.</li> <li><a href="/NVIDIA/cutlass/blob/main/include/cutlass/epilogue/collective/sm100_epilogue_tma_warpspecialized.hpp">Blackwell epilogue that supports loading accumulators from <code>tmem</code></a> and <a href="/NVIDIA/cutlass/blob/main">full set of EVT fusions</a>.</li> </ul> </li> <li>CUTLASS library and profiler integration for block scaled data types for kernel emission, profiling, and verification. <ul dir="auto"> <li>Support for preferred and fallback cluster shapes via profiler command line arguments parsing to set dynamic cluster shapes.</li> <li>Support for dynamic datatypes by parsing profiler via profiler command line arguments parsing to set dynamic datatype setting in TCGen05 MMA instruction descriptors.</li> </ul> </li> <li>Set of examples that demonstrate the usage of the 3.x API for targeting Blackwell <ul dir="auto"> <li><a href="/NVIDIA/cutlass/blob/main/examples/70_blackwell_gemm">Basic FP16 and FP8 GEMMs with minimal changes from Hopper examples</a>, demonstrating ease of migration for off the shelf kernels using the 3.x collective builder API.</li> <li>GEMM with <a href="/NVIDIA/cutlass/blob/main/examples/71_blackwell_gemm_with_collective_builder/71_blackwell_gemm_with_collective_builder.cu">opt-in collective builder schedules showcasing available recipes</a> for Blackwell.</li> <li>Block scaled data type GEMMs targeting Blackwell's native block scaled Tensor Cores: <ul dir="auto"> <li><a href="/NVIDIA/cutlass/blob/main/examples/72_blackwell_narrow_precision_gemm/72a_blackwell_nvfp4_bf16_gemm.cu">NVFP4 inputs with BF16 output</a></li> <li><a href="/NVIDIA/cutlass/blob/main/examples/72_blackwell_narrow_precision_gemm/72b_blackwell_nvfp4_nvfp4_gemm.cu">NVFP4 inputs with NVFP4 output</a></li> <li><a href="/NVIDIA/cutlass/blob/main/examples/72_blackwell_narrow_precision_gemm/72c_blackwell_mixed_mxfp8_bf16_gemm.cu">Mixed MXFP8 and MXFP6 inputs with BF16 output</a></li> </ul> </li> <li>GEMM example demonstrating <a href="/NVIDIA/cutlass/blob/main/examples/73_blackwell_gemm_preferred_cluster/blackwell_gemm_preferred_cluster.cu">Blackwell's new preferred cluster support via dynamic cluster shapes</a> for increased occupancy.</li> <li><a href="/NVIDIA/cutlass/blob/main/examples/74_blackwell_gemm_streamk/blackwell_gemm_streamk.cu">GEMM with CLC based StreamK scheduler for load balancing</a>.</li> <li>Grouped GEMM for <a href="/NVIDIA/cutlass/blob/main/examples/75_blackwell_grouped_gemm/75_blackwell_grouped_gemm.cu">vanilla FP8 data inputs</a> and <a href="/NVIDIA/cutlass/blob/main/examples/75_blackwell_grouped_gemm/75_blackwell_grouped_gemm_block_scaled.cu">NVFP4 block scaled inputs</a>.</li> <li>Convolution kernels for <a href="/NVIDIA/cutlass/blob/main/examples/76_blackwell_conv/76_blackwell_conv_fprop.cu">fprop</a>, <a href="/NVIDIA/cutlass/blob/main/examples/76_blackwell_conv/76_blackwell_conv_dgrad.cu">dgrad</a>, and <a href="/NVIDIA/cutlass/blob/main/examples/76_blackwell_conv/76_blackwell_conv_wgrad.cu">wgrad</a>.</li> <li><a href="/NVIDIA/cutlass/blob/main/examples/77_blackwell_fmha/77_blackwell_fmha.cu">Fused multi-head attention fprop kernel</a> supporting fp16/bf16/fp8 data types across head dims of 32,64, and 128.</li> </ul> </li> <li>Documentation updates: <ul dir="auto"> <li><a href="/NVIDIA/cutlass/blob/main/media/docs/quickstart.md#instantiating-a-blackwell-gemm-kernel">Quickstart - instantiating a Blackwell block-scaled GEMM</a>.</li> <li>Detailed <a href="/NVIDIA/cutlass/blob/main/media/docs/blackwell_functionality.md">Blackwell block-scaled GEMM functionality documentation</a></li> <li>A new <a href="/NVIDIA/cutlass/blob/main/media/docs/functionality.md">functionality documentation</a> specifically for 3.x API comprehensively documenting all supported kernel types, data types, kernel features, minimum CUDA tookit support etc for 3.x supported architectures.</li> <li>Updates to <a href="/NVIDIA/cutlass/blob/main/README.md#compatibility">compatibility</a> section regarding supported compilers, operating systems, CUDA Toolkits, Hardware Architectures, and <a href="/NVIDIA/cutlass/blob/main/README.md#Target-Architecture">Target Architecture</a>.</li> </ul> </li> </ul> <p dir="auto">Note: CUTLASS 3.x builds are known to be down on Windows platforms for all CUDA toolkits. CUTLASS team is working on a fix.</p> <p dir="auto"><strong>See the <a href="/NVIDIA/cutlass/blob/main/CHANGELOG.md">CHANGELOG</a> for details of all past releases and updates.</strong></p> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">Performance</h1><a id="user-content-performance" class="anchor" aria-label="Permalink: Performance" href="#performance"><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">CUTLASS primitives are very efficient. When used to construct device-wide GEMM kernels, they exhibit nearly optimal utilization of peak theoretical throughput. The figure below shows CUTLASS 3.8's performance as a % of theoretical peak utilization on various input and output data types when run on NVIDIA Blackwell SM100 architecture GPU.</p> <p align="center" dir="auto"><a target="_blank" rel="noopener noreferrer" href="/NVIDIA/cutlass/blob/main/media/images/cutlass-3.8-blackwell-gemm-peak-performance.svg"><img src="/NVIDIA/cutlass/raw/main/media/images/cutlass-3.8-blackwell-gemm-peak-performance.svg" style="max-width: 100%;"></a></p> <p dir="auto">The two figures below show the continual CUTLASS performance improvements on an <a href="https://www.nvidia.com/en-us/data-center/h100/" rel="nofollow">NVIDIA H100</a> (NVIDIA Hopper architecture) since CUTLASS 3.1. CUTLASS 3.5.1 was compiled with the <a href="https://developer.nvidia.com/cuda-downloads" rel="nofollow">CUDA 12.5u1 Toolkit</a>. Tensor Core operations are implemented using CUDA's <a href="https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#warp-level-matrix-instructions-mma" rel="nofollow">mma</a> and <a href="https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#asynchronous-warpgroup-level-matrix-instructions" rel="nofollow">wgmma</a> instructions.</p> <p align="center" dir="auto"><a target="_blank" rel="noopener noreferrer" href="/NVIDIA/cutlass/blob/main/media/images/cutlass-3.5.1-gemm-peak-performance.png"><img src="/NVIDIA/cutlass/raw/main/media/images/cutlass-3.5.1-gemm-peak-performance.png" style="max-width: 100%;"></a></p> <p align="center" dir="auto"><a target="_blank" rel="noopener noreferrer" href="/NVIDIA/cutlass/blob/main/media/images/cutlass-3.5.1-gemm-peak-performance-fp8.png"><img src="/NVIDIA/cutlass/raw/main/media/images/cutlass-3.5.1-gemm-peak-performance-fp8.png" style="max-width: 100%;"></a></p> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">CuTe</h1><a id="user-content-cute" class="anchor" aria-label="Permalink: CuTe" href="#cute"><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">CUTLASS 3.0 introduced a new core library, CuTe, to describe and manipulate tensors of threads and data. CuTe is a collection of C++ CUDA template abstractions for defining and operating on hierarchically multidimensional layouts of threads and data. CuTe provides <code>Layout</code> and <code>Tensor</code> objects that compactly package the type, shape, memory space, and layout of data, while performing the complicated indexing for the user. This lets programmers focus on the logical descriptions of their algorithms while CuTe does the mechanical bookkeeping for them. With these tools, we can quickly design, implement, and modify all dense linear algebra operations.</p> <p dir="auto">The core abstractions of CuTe are hierarchically multidimensional layouts which can be composed with data arrays to represent tensors. The representation of layouts is powerful enough to represent nearly everything we need to implement efficient dense linear algebra. Layouts can also be combined and manipulated via functional composition, on which we build a large set of common operations such as tiling and partitioning.</p> <p dir="auto">CUTLASS 3.0 and beyond adopts CuTe throughout the GEMM hierarchy in its templates. This greatly simplifies the design and improves code composability and readability. More documentation specific to CuTe can be found in its <a href="/NVIDIA/cutlass/blob/main/media/docs/cute/00_quickstart.md">dedicated documentation directory</a>.</p> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">Compatibility</h1><a id="user-content-compatibility" class="anchor" aria-label="Permalink: Compatibility" href="#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">Minimum requirements:</p> <ul dir="auto"> <li>Architecture: Volta (compute capability 7.0)</li> <li>Compiler: Must support at least C++17</li> <li>CUDA Toolkit version: 11.4</li> </ul> <p dir="auto">CUTLASS requires a C++17 host compiler and performs best when built with the <a href="https://developer.nvidia.com/cuda-downloads" rel="nofollow"><strong>CUDA 12.8 Toolkit</strong></a>. It is also compatible with CUDA 11.4, CUDA 11.5, CUDA 11.6, CUDA 11.7, CUDA 11.8, and all other CUDA 12.x versions.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Operating Systems</h2><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">We have tested the following environments.</p> <markdown-accessiblity-table><table> <thead> <tr> <th><strong>Operating System</strong></th> <th><strong>Compiler</strong></th> </tr> </thead> <tbody> <tr> <td>Ubuntu 18.04</td> <td>GCC 7.5.0</td> </tr> <tr> <td>Ubuntu 20.04</td> <td>GCC 10.3.0</td> </tr> <tr> <td>Ubuntu 22.04</td> <td>GCC 11.2.0</td> </tr> </tbody> </table></markdown-accessiblity-table> <p dir="auto">Note: GCC 8.5.0 has known regressions regarding fold expressions and overloaded operators. Using GCC 7.5.0 or (preferred) GCC >= 9 is recommended.</p> <p dir="auto">Note: CUTLASS 3.x builds are known to be down on Windows platforms for all CUDA toolkits. CUTLASS team is working on a fix.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Hardware</h2><a id="user-content-hardware" class="anchor" aria-label="Permalink: Hardware" href="#hardware"><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">CUTLASS runs successfully on the following NVIDIA GPUs, and it is expected to be efficient on Volta, Turing, Ampere, Ada, and Hopper architecture based NVIDIA GPUs.</p> <markdown-accessiblity-table><table> <thead> <tr> <th><strong>GPU</strong></th> <th><strong>CUDA Compute Capability</strong></th> <th><strong>Minimum CUDA Toolkit Required by CUTLASS-3</strong></th> </tr> </thead> <tbody> <tr> <td>NVIDIA V100 Tensor Core GPU</td> <td>7.0</td> <td>11.4</td> </tr> <tr> <td>NVIDIA TitanV</td> <td>7.0</td> <td>11.4</td> </tr> <tr> <td>NVIDIA GeForce RTX 20x0 series</td> <td>7.5</td> <td>11.4</td> </tr> <tr> <td>NVIDIA T4</td> <td>7.5</td> <td>11.4</td> </tr> <tr> <td>NVIDIA A100 Tensor Core GPU</td> <td>8.0</td> <td>11.4</td> </tr> <tr> <td>NVIDIA A10</td> <td>8.6</td> <td>11.4</td> </tr> <tr> <td>NVIDIA GeForce RTX 30x0 series</td> <td>8.6</td> <td>11.4</td> </tr> <tr> <td>NVIDIA GeForce RTX 40x0 series</td> <td>8.9</td> <td>11.8</td> </tr> <tr> <td>NVIDIA L40</td> <td>8.9</td> <td>11.8</td> </tr> <tr> <td>NVIDIA H100 Tensor Core GPU</td> <td>9.0</td> <td>11.8</td> </tr> <tr> <td>NVIDIA H200 Tensor Core GPU</td> <td>9.0</td> <td>11.8</td> </tr> <tr> <td>NVIDIA B200 Tensor Core GPU</td> <td>10.0</td> <td>12.8</td> </tr> </tbody> </table></markdown-accessiblity-table> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Target Architecture</h2><a id="user-content-target-architecture" class="anchor" aria-label="Permalink: Target Architecture" href="#target-architecture"><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">In general, PTX code generated for one target architecture can be run on future architectures (i.e., it is forward compatible). However, CUDA 12.0 introduced the concept of "architecture-accelerated features" whose PTX does not have forward compatibility guarantees. Several Hopper and Blackwell PTX instructions fall under this category of architecture-accelerated features, and thus require a <code>sm_90a</code> or <code>sm100a</code> target architecture (note the "a" appended). For more details on this and other architecture-accelerated instructions, please refer to the <a href="https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#feature-availability" rel="nofollow">CUDA Documentation</a>.</p> <p dir="auto">The target architecture information is passed on to CUTLASS via the cmake flag <code>CUTLASS_NVCC_ARCHS</code>. In order to maximize performance on Hopper GH100, users are required to build CUTLASS with <code>90a</code> as the target architecture. If a user accidentally builds a kernel which uses SM90a features (e.g. Hopper Tensor Core Instructions), using the SM90 target (note the lack of "a"), with either CUDA Toolkit 12 or 11.8, the kernel is expected to fail with a runtime error.</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="cmake .. -DCUTLASS_NVCC_ARCHS="90a""><pre class="notranslate"><code>cmake .. -DCUTLASS_NVCC_ARCHS="90a" </code></pre></div> <p dir="auto">Or</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="cmake .. -DCUTLASS_NVCC_ARCHS="100a" "><pre class="notranslate"><code>cmake .. -DCUTLASS_NVCC_ARCHS="100a" </code></pre></div> <p dir="auto">Note: The NVIDIA Blackwell SM100 architecture used in the datacenter products has a different compute capability than the one underpinning NVIDIA Blackwell GeForce RTX 50 series GPUs. As a result, kernels compiled for Blackwell SM100 architecture with arch conditional features (using <code>sm100a</code>) are not compatible with RTX 50 series GPUs.</p> <p dir="auto">Please refer to the <a href="/NVIDIA/cutlass/blob/main/media/docs/functionality.md">functionality documentation</a> for details on which kernels require which target architectures.</p> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">Documentation</h1><a id="user-content-documentation" class="anchor" aria-label="Permalink: Documentation" href="#documentation"><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">CUTLASS is described in the following documents and the accompanying <a href="https://nvidia.github.io/cutlass" rel="nofollow">Doxygen documentation</a>.</p> <ul dir="auto"> <li><a href="/NVIDIA/cutlass/blob/main/media/docs/quickstart.md">Quick Start Guide</a> - basics of building and running CUTLASS</li> <li><a href="/NVIDIA/cutlass/blob/main/media/docs/functionality.md">Functionality</a> - summarizes functionality available in CUTLASS</li> <li><a href="/NVIDIA/cutlass/blob/main/media/docs/efficient_gemm.md">Efficient GEMM in CUDA</a> - describes how GEMM kernels may be implemented efficiently in CUDA</li> <li><a href="/NVIDIA/cutlass/blob/main/media/docs/cutlass_3x_design.md">CUTLASS 3.x Design</a> - describes the CUTLASS 3.x design, its benefits, and how CuTe enables us to write much more composable components</li> <li><a href="/NVIDIA/cutlass/blob/main/media/docs/gemm_api_3x.md">GEMM API 3.x</a> - describes the CUTLASS 3.x GEMM model and C++ template concepts</li> <li><a href="/NVIDIA/cutlass/blob/main/media/docs/gemm_api.md">GEMM API 2.x</a> - describes the CUTLASS 2.x GEMM model and C++ template concepts</li> <li><a href="/NVIDIA/cutlass/blob/main/media/docs/implicit_gemm_convolution.md">Implicit GEMM Convolution</a> - describes 2-D and 3-D convolution in CUTLASS</li> <li><a href="/NVIDIA/cutlass/blob/main/media/docs/code_organization.md">Code Organization</a> - describes the organization and contents of the CUTLASS project</li> <li><a href="/NVIDIA/cutlass/blob/main/media/docs/terminology.md">Terminology</a> - describes terms used in the code</li> <li><a href="/NVIDIA/cutlass/blob/main/media/docs/programming_guidelines.md">Programming Guidelines</a> - guidelines for writing efficient modern CUDA C++</li> <li><a href="/NVIDIA/cutlass/blob/main/media/docs/fundamental_types.md">Fundamental types</a> - describes basic C++ classes used in CUTLASS to represent numeric quantities and arrays</li> <li><a href="/NVIDIA/cutlass/blob/main/media/docs/layout.md">Layouts</a> - describes layouts of matrices and tensors in memory</li> <li><a href="/NVIDIA/cutlass/blob/main/media/docs/tile_iterator_concept.md">Tile Iterators</a> - describes C++ concepts for iterating over tiles of matrices in memory</li> <li><a href="/NVIDIA/cutlass/blob/main/media/docs/profiler.md">CUTLASS Profiler</a> - command-line driven profiling application</li> <li><a href="/NVIDIA/cutlass/blob/main/media/docs/utilities.md">CUTLASS Utilities</a> - additional templates used to facilitate rapid development</li> <li><a href="/NVIDIA/cutlass/blob/main/media/docs/dependent_kernel_launch.md">Dependent kernel launch</a> - describes a new feature in Hopper which allows overlapping dependent kernels in the same stream, and how it is used in CUTLASS.</li> </ul> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">Resources</h1><a id="user-content-resources" class="anchor" aria-label="Permalink: Resources" href="#resources"><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 have also described the structure of an efficient GEMM in our talk at the <a href="http://on-demand.gputechconf.com/gtc/2018/presentation/s8854-cutlass-software-primitives-for-dense-linear-algebra-at-all-levels-and-scales-within-cuda.pdf" rel="nofollow">GPU Technology Conference 2018</a>.</p> <ul dir="auto"> <li><a href="https://www.nvidia.com/en-us/on-demand/session/gtcsiliconvalley2018-s8854/" rel="nofollow">CUTLASS: Software Primitives for Dense Linear Algebra at All Levels and Scales within CUDA</a></li> <li><a href="https://www.nvidia.com/en-us/on-demand/session/gtcsj20-s21745/" rel="nofollow">Developing CUDA Kernels to Push Tensor Cores to the Absolute Limit on NVIDIA A100</a></li> <li><a href="https://www.nvidia.com/en-us/on-demand/session/gtcspring21-s31883/" rel="nofollow">Accelerating Convolution with Tensor Cores in CUTLASS</a></li> <li><a href="https://www.nvidia.com/en-us/on-demand/session/gtcspring22-s41996/" rel="nofollow">Accelerating Backward Data Gradient by Increasing Tensor Core Utilization in CUTLASS</a></li> <li><a href="https://www.nvidia.com/en-us/on-demand/session/gtcfall22-a41131/" rel="nofollow">CUTLASS: Python API, Enhancements, and NVIDIA Hopper</a></li> </ul> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">Building CUTLASS</h1><a id="user-content-building-cutlass" class="anchor" aria-label="Permalink: Building CUTLASS" href="#building-cutlass"><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">CUTLASS is a header-only template library and does not need to be built to be used by other projects. Client applications should target CUTLASS's <code>include/</code> directory in their include paths.</p> <p dir="auto">CUTLASS unit tests, examples, and utilities can be build with CMake. The minimum version of CMake is given in the <a href="/NVIDIA/cutlass/blob/main/media/docs/quickstart.md">Quickstart guide</a>. Make sure the <code>CUDACXX</code> environment variable points to NVCC in the CUDA Toolkit installed on your system.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="$ export CUDACXX=${CUDA_INSTALL_PATH}/bin/nvcc"><pre>$ <span class="pl-k">export</span> CUDACXX=<span class="pl-smi">${CUDA_INSTALL_PATH}</span>/bin/nvcc</pre></div> <p dir="auto">Create a build directory within the CUTLASS project, then run CMake. By default CUTLASS will build kernels for CUDA architecture versions 5.0, 6.0, 6.1, 7.0, 7.5, 8.0, 8.6, 8.9, and 9.0. To reduce compile time you can specify the architectures to build CUTLASS for by changing the CMake configuration setting <code>CUTLASS_NVCC_ARCHS</code>.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="$ mkdir build && cd build $ cmake .. -DCUTLASS_NVCC_ARCHS=80 # compiles for NVIDIA's Ampere Architecture"><pre>$ mkdir build <span class="pl-k">&&</span> <span class="pl-c1">cd</span> build $ cmake .. -DCUTLASS_NVCC_ARCHS=80 <span class="pl-c"><span class="pl-c">#</span> compiles for NVIDIA's Ampere Architecture</span></pre></div> <p dir="auto">From the <code>build/</code> directory, compile and run the CUTLASS unit tests by building the target <code>test_unit</code> with make.</p> <p dir="auto">The unit tests are organized as several binaries mirroring the top-level namespaces of CUTLASS, and they may be executed in parallel via make's <code>-j</code> command line argument.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="$ make test_unit -j ... ... ... [----------] Global test environment tear-down [==========] 946 tests from 57 test cases ran. (10812 ms total) [ PASSED ] 946 tests."><pre>$ make test_unit -j ... ... ... [----------] Global <span class="pl-c1">test</span> environment tear-down [<span class="pl-k">==========</span>] 946 tests from 57 <span class="pl-c1">test</span> cases ran. (10812 ms total) [ PASSED ] 946 tests.</pre></div> <p dir="auto">All tests should pass on supported platforms, though the exact number of tests may vary over time.</p> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">Project Structure</h1><a id="user-content-project-structure" class="anchor" aria-label="Permalink: Project Structure" href="#project-structure"><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">CUTLASS is arranged as a header-only library along with Utilities, Tools, Examples, and unit tests. <a href="https://nvidia.github.io/cutlass" rel="nofollow">Doxygen documentation</a> provides a complete list of files, classes, and template concepts defined in the CUTLASS project.</p> <p dir="auto">A detailed explanation of the source code organization may be found in the <a href="/NVIDIA/cutlass/blob/main/media/docs/code_organization.md">CUTLASS documentation</a>, but several main components are summarized below.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">CUTLASS Template Library</h2><a id="user-content-cutlass-template-library" class="anchor" aria-label="Permalink: CUTLASS Template Library" href="#cutlass-template-library"><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="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="include/ # client applications should target this directory in their build's include paths cutlass/ # CUDA Templates for Linear Algebra Subroutines and Solvers - headers only arch/ # direct exposure of architecture features (including instruction-level GEMMs) conv/ # code specialized for convolution epilogue/ # code specialized for the epilogue of gemm/convolution gemm/ # code specialized for general matrix product computations layout/ # layout definitions for matrices, tensors, and other mathematical objects in memory platform/ # CUDA-capable Standard Library components reduction/ # bandwidth-limited reduction kernels that do not fit the "gemm" model thread/ # simt code that can be performed within a CUDA thread transform/ # code specialized for layout, type, and domain transformations * # core vocabulary types, containers, and basic numeric operations cute/ # CuTe Layout, layout algebra, MMA/Copy atoms, tiled MMA/Copy algorithm/ # Definitions of core operations such as copy, gemm, and operations on cute::tuples arch/ # Bare bones PTX wrapper structs for copy and math instructions atom/ # Meta-information either link to or built from arch/ operators mma_atom.hpp # cute::Mma_Atom and cute::TiledMma copy_atom.hpp # cute::Copy_Atom and cute::TiledCopy *sm*.hpp # Arch specific meta-information for copy and math operations * # Core library types such as Shape, Stride, Layout, Tensor, and associated operations "><pre class="notranslate"><code>include/ # client applications should target this directory in their build's include paths cutlass/ # CUDA Templates for Linear Algebra Subroutines and Solvers - headers only arch/ # direct exposure of architecture features (including instruction-level GEMMs) conv/ # code specialized for convolution epilogue/ # code specialized for the epilogue of gemm/convolution gemm/ # code specialized for general matrix product computations layout/ # layout definitions for matrices, tensors, and other mathematical objects in memory platform/ # CUDA-capable Standard Library components reduction/ # bandwidth-limited reduction kernels that do not fit the "gemm" model thread/ # simt code that can be performed within a CUDA thread transform/ # code specialized for layout, type, and domain transformations * # core vocabulary types, containers, and basic numeric operations cute/ # CuTe Layout, layout algebra, MMA/Copy atoms, tiled MMA/Copy algorithm/ # Definitions of core operations such as copy, gemm, and operations on cute::tuples arch/ # Bare bones PTX wrapper structs for copy and math instructions atom/ # Meta-information either link to or built from arch/ operators mma_atom.hpp # cute::Mma_Atom and cute::TiledMma copy_atom.hpp # cute::Copy_Atom and cute::TiledCopy *sm*.hpp # Arch specific meta-information for copy and math operations * # Core library types such as Shape, Stride, Layout, Tensor, and associated operations </code></pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">CUTLASS SDK Examples</h3><a id="user-content-cutlass-sdk-examples" class="anchor" aria-label="Permalink: CUTLASS SDK Examples" href="#cutlass-sdk-examples"><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 href="/NVIDIA/cutlass/blob/main/examples">CUTLASS SDK examples</a> apply CUTLASS templates to implement basic computations.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Tools</h3><a id="user-content-tools" class="anchor" aria-label="Permalink: Tools" href="#tools"><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="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="tools/ library/ # CUTLASS Instance Library - contains instantiations of all supported CUTLASS templates include/ cutlass/ library/ profiler/ # CUTLASS Profiler - command-line utility for executing operations in the # CUTLASS Library util/ # CUTLASS Utilities - contains numerous helper classes for include/ # manging tensors in device memory, reference cutlass/ # implementations for GEMM, random initialization util/ # of tensors, and I/O."><pre class="notranslate"><code>tools/ library/ # CUTLASS Instance Library - contains instantiations of all supported CUTLASS templates include/ cutlass/ library/ profiler/ # CUTLASS Profiler - command-line utility for executing operations in the # CUTLASS Library util/ # CUTLASS Utilities - contains numerous helper classes for include/ # manging tensors in device memory, reference cutlass/ # implementations for GEMM, random initialization util/ # of tensors, and I/O. </code></pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Test</h3><a id="user-content-test" class="anchor" aria-label="Permalink: Test" href="#test"><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 <code>test/unit/</code> directory consist of unit tests implemented with Google Test that demonstrate basic usage of Core API components and complete tests of the CUTLASS GEMM computations.</p> <p dir="auto">Instructions for building and running the Unit tests are described in the <a href="/NVIDIA/cutlass/blob/main/media/docs/quickstart.md">Quickstart guide</a>.</p> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">Performance Profiling</h1><a id="user-content-performance-profiling" class="anchor" aria-label="Permalink: Performance Profiling" href="#performance-profiling"><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 <code>tools/profiler/</code> directory contains a command-line utility for launching each of the GEMM kernels. It can be built as follows:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="$ make cutlass_profiler -j16"><pre>$ make cutlass_profiler -j16</pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Building all GEMM and Convolution kernels (<em>long</em> build times)</h2><a id="user-content-building-all-gemm-and-convolution-kernels-long-build-times" class="anchor" aria-label="Permalink: Building all GEMM and Convolution kernels (long build times)" href="#building-all-gemm-and-convolution-kernels-long-build-times"><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">By default, only one tile size is instantiated for each data type, math instruction, and layout. To instantiate all, set the following environment variable when running CMake from an empty <code>build/</code> directory. Beware, this results in <em>tens of thousands</em> of kernels and long build times. This would also result in a large binary size and on some platforms linker to fail on building the library. Therefore, it's highly recommended to generate only a subset of kernels as demonstrated in the sub-section below.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="$ cmake .. -DCUTLASS_NVCC_ARCHS=90a -DCUTLASS_LIBRARY_KERNELS=all ... $ make cutlass_profiler -j16"><pre>$ cmake .. -DCUTLASS_NVCC_ARCHS=90a -DCUTLASS_LIBRARY_KERNELS=all ... $ make cutlass_profiler -j16</pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Building a subset of GEMM and Convolution kernels (<em>reduced</em> build times)</h2><a id="user-content-building-a-subset-of-gemm-and-convolution-kernels-reduced-build-times" class="anchor" aria-label="Permalink: Building a subset of GEMM and Convolution kernels (reduced build times)" href="#building-a-subset-of-gemm-and-convolution-kernels-reduced-build-times"><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">To compile strictly one kernel or a small set of kernels, a comma-delimited list of kernel names with wildcard characters may be used to reduce the set of kernels. The following examples show building exactly one or a subset of kernels for NVIDIA Ampere and Turing architecture:</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Building a subset Tensor Core GEMM kernels</h3><a id="user-content-building-a-subset-tensor-core-gemm-kernels" class="anchor" aria-label="Permalink: Building a subset Tensor Core GEMM kernels" href="#building-a-subset-tensor-core-gemm-kernels"><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">To compile a subset of Tensor Core GEMM kernels with FP32 accumulation and FP16 input targeting NVIDIA Ampere and Turing architecture, use the below cmake command line:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="$ cmake .. -DCUTLASS_NVCC_ARCHS='75;80' -DCUTLASS_LIBRARY_KERNELS=cutlass_tensorop_s*gemm_f16_*_nt_align8 ... $ make cutlass_profiler -j16"><pre>$ cmake .. -DCUTLASS_NVCC_ARCHS=<span class="pl-s"><span class="pl-pds">'</span>75;80<span class="pl-pds">'</span></span> -DCUTLASS_LIBRARY_KERNELS=cutlass_tensorop_s<span class="pl-k">*</span>gemm_f16_<span class="pl-k">*</span>_nt_align8 ... $ make cutlass_profiler -j16</pre></div> <p dir="auto">Example command line for profiling a subset of Tensor Core GEMM kernels is as follows:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="./tools/profiler/cutlass_profiler --kernels=cutlass_tensorop_s*gemm_f16_*_nt_align8 --m=3456 --n=4096 --k=4096 ... ============================= Problem ID: 1 Provider: CUTLASS OperationKind: gemm Operation: cutlass_tensorop_s1688gemm_f16_256x128_32x2_nt_align8 Status: Success Verification: ON Disposition: Passed reference_device: Passed cuBLAS: Passed Arguments: --gemm_kind=universal --m=3456 --n=4096 --k=4096 --A=f16:column --B=f16:row --C=f32:column --alpha=1 \ --beta=0 --split_k_slices=1 --batch_count=1 --op_class=tensorop --accum=f32 --cta_m=256 --cta_n=128 \ --cta_k=32 --stages=2 --warps_m=4 --warps_n=2 --warps_k=1 --inst_m=16 --inst_n=8 --inst_k=8 --min_cc=75 \ --max_cc=1024 Bytes: 118489088 bytes FLOPs: 115992428544 flops Runtime: 1.55948 ms Memory: 70.7616 GiB/s Math: 74378.8 GFLOP/s ============================= ..."><pre>./tools/profiler/cutlass_profiler --kernels=cutlass_tensorop_s<span class="pl-k">*</span>gemm_f16_<span class="pl-k">*</span>_nt_align8 --m=3456 --n=4096 --k=4096 ... ============================= Problem ID: 1 Provider: CUTLASS OperationKind: gemm Operation: cutlass_tensorop_s1688gemm_f16_256x128_32x2_nt_align8 Status: Success Verification: ON Disposition: Passed reference_device: Passed cuBLAS: Passed Arguments: --gemm_kind=universal --m=3456 --n=4096 --k=4096 --A=f16:column --B=f16:row --C=f32:column --alpha=1 \ --beta=0 --split_k_slices=1 --batch_count=1 --op_class=tensorop --accum=f32 --cta_m=256 --cta_n=128 \ --cta_k=32 --stages=2 --warps_m=4 --warps_n=2 --warps_k=1 --inst_m=16 --inst_n=8 --inst_k=8 --min_cc=75 \ --max_cc=1024 Bytes: 118489088 bytes FLOPs: 115992428544 flops Runtime: 1.55948 ms Memory: 70.7616 GiB/s Math: 74378.8 GFLOP/s ============================= ...</pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Building one CUDA Core GEMM kernel</h3><a id="user-content-building-one-cuda-core-gemm-kernel" class="anchor" aria-label="Permalink: Building one CUDA Core GEMM kernel" href="#building-one-cuda-core-gemm-kernel"><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">To compile one SGEMM kernel targeting NVIDIA Ampere and Turing architecture, use the below cmake command line:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="$ cmake .. -DCUTLASS_NVCC_ARCHS='75;80' -DCUTLASS_LIBRARY_KERNELS=cutlass_simt_sgemm_128x128_8x2_nn_align1 ... $ make cutlass_profiler -j16"><pre>$ cmake .. -DCUTLASS_NVCC_ARCHS=<span class="pl-s"><span class="pl-pds">'</span>75;80<span class="pl-pds">'</span></span> -DCUTLASS_LIBRARY_KERNELS=cutlass_simt_sgemm_128x128_8x2_nn_align1 ... $ make cutlass_profiler -j16</pre></div> <p dir="auto">Example command line for profiling single SGEMM CUDA kernel is as follows:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="$ ./tools/profiler/cutlass_profiler --kernels=sgemm --m=3456 --n=4096 --k=4096 ============================= Problem ID: 1 Provider: CUTLASS OperationKind: gemm Operation: cutlass_simt_sgemm_128x128_8x2_nn_align1 Status: Success Verification: ON Disposition: Passed cuBLAS: Passed Arguments: --m=3456 --n=4096 --k=4096 --A=f32:column --B=f32:column --C=f32:column --alpha=1 --beta=0 --split_k_slices=1 \ --batch_count=1 --op_class=simt --accum=f32 --cta_m=128 --cta_n=128 --cta_k=8 --stages=2 --warps_m=4 \ --warps_n=2 --warps_k=1 --inst_m=1 --inst_n=1 --inst_k=1 --min_cc=50 --max_cc=1024 Bytes: 180355072 bytes FLOPs: 115992428544 flops Runtime: 6.73655 ms Memory: 24.934 GiB/s Math: 17218.4 GFLOP/s ============================="><pre>$ ./tools/profiler/cutlass_profiler --kernels=sgemm --m=3456 --n=4096 --k=4096 ============================= Problem ID: 1 Provider: CUTLASS OperationKind: gemm Operation: cutlass_simt_sgemm_128x128_8x2_nn_align1 Status: Success Verification: ON Disposition: Passed cuBLAS: Passed Arguments: --m=3456 --n=4096 --k=4096 --A=f32:column --B=f32:column --C=f32:column --alpha=1 --beta=0 --split_k_slices=1 \ --batch_count=1 --op_class=simt --accum=f32 --cta_m=128 --cta_n=128 --cta_k=8 --stages=2 --warps_m=4 \ --warps_n=2 --warps_k=1 --inst_m=1 --inst_n=1 --inst_k=1 --min_cc=50 --max_cc=1024 Bytes: 180355072 bytes FLOPs: 115992428544 flops Runtime: 6.73655 ms Memory: 24.934 GiB/s Math: 17218.4 GFLOP/s =============================</pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Building a subset of Tensor Core Convolution kernels</h3><a id="user-content-building-a-subset-of-tensor-core-convolution-kernels" class="anchor" aria-label="Permalink: Building a subset of Tensor Core Convolution kernels" href="#building-a-subset-of-tensor-core-convolution-kernels"><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">To compile a subset of Tensor core convolution kernels implementing forward propagation (fprop) with FP32 accumulation and FP16 input targeting NVIDIA Ampere and Turing architecture, use the below cmake command line:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="$ cmake .. -DCUTLASS_NVCC_ARCHS='75;80' -DCUTLASS_LIBRARY_KERNELS=cutlass_tensorop_s*fprop_optimized_f16 ... $ make cutlass_profiler -j16"><pre>$ cmake .. -DCUTLASS_NVCC_ARCHS=<span class="pl-s"><span class="pl-pds">'</span>75;80<span class="pl-pds">'</span></span> -DCUTLASS_LIBRARY_KERNELS=cutlass_tensorop_s<span class="pl-k">*</span>fprop_optimized_f16 ... $ make cutlass_profiler -j16</pre></div> <p dir="auto">Example command line for profiling a subset of Tensor Core convolution kernels is as follows:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="$ ./tools/profiler/cutlass_profiler --kernels=cutlass_tensorop_s*fprop_optimized_f16 --n=8 --h=224 --w=224 --c=128 --k=128 --r=3 --s=3 ... ============================= Problem ID: 1 Provider: CUTLASS OperationKind: conv2d Operation: cutlass_tensorop_s16816fprop_optimized_f16_128x128_32x5_nhwc Status: Success Verification: ON Disposition: Passed reference_device: Passed Arguments: --conv_kind=fprop --n=8 --h=224 --w=224 --c=128 --k=128 --r=3 --s=3 --p=224 --q=224 --pad_h=1 --pad_w=1 \ --stride_h=1 --stride_w=1 --dilation_h=1 --dilation_w=1 --Activation=f16:nhwc --Filter=f16:nhwc --Output=f32:nhwc \ --conv_mode=cross --iterator_algorithm=optimized --alpha=1 --beta=0 --split_k_mode=serial --split_k_slices=1 \ --eq_gemm_provider=none --op_class=tensorop --accum=f32 --cta_m=128 --cta_n=128 --cta_k=32 --stages=5 \ --warps_m=2 --warps_n=2 --warps_k=1 --inst_m=16 --inst_n=8 --inst_k=16 --min_cc=80 --max_cc=1024 Bytes: 1130659840 bytes FLOPs: 118482796544 flops Runtime: 0.711496 ms Memory: 1479.99 GiB/s Math: 166526 GFLOP/s ============================= ..."><pre>$ ./tools/profiler/cutlass_profiler --kernels=cutlass_tensorop_s<span class="pl-k">*</span>fprop_optimized_f16 --n=8 --h=224 --w=224 --c=128 --k=128 --r=3 --s=3 ... ============================= Problem ID: 1 Provider: CUTLASS OperationKind: conv2d Operation: cutlass_tensorop_s16816fprop_optimized_f16_128x128_32x5_nhwc Status: Success Verification: ON Disposition: Passed reference_device: Passed Arguments: --conv_kind=fprop --n=8 --h=224 --w=224 --c=128 --k=128 --r=3 --s=3 --p=224 --q=224 --pad_h=1 --pad_w=1 \ --stride_h=1 --stride_w=1 --dilation_h=1 --dilation_w=1 --Activation=f16:nhwc --Filter=f16:nhwc --Output=f32:nhwc \ --conv_mode=cross --iterator_algorithm=optimized --alpha=1 --beta=0 --split_k_mode=serial --split_k_slices=1 \ --eq_gemm_provider=none --op_class=tensorop --accum=f32 --cta_m=128 --cta_n=128 --cta_k=32 --stages=5 \ --warps_m=2 --warps_n=2 --warps_k=1 --inst_m=16 --inst_n=8 --inst_k=16 --min_cc=80 --max_cc=1024 Bytes: 1130659840 bytes FLOPs: 118482796544 flops Runtime: 0.711496 ms Memory: 1479.99 GiB/s Math: 166526 GFLOP/s ============================= ...</pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Building one Convolution CUDA kernel</h3><a id="user-content-building-one-convolution-cuda-kernel" class="anchor" aria-label="Permalink: Building one Convolution CUDA kernel" href="#building-one-convolution-cuda-kernel"><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">To compile and run one CUDA Core convolution kernel implementing forward propagation (fprop) with F32 accumulation and FP32 input targeting NVIDIA Ampere and Turing architecture, use the below cmake command line:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="$ cmake .. -DCUTLASS_NVCC_ARCHS='75;80' -DCUTLASS_LIBRARY_KERNELS=cutlass_simt_sfprop_optimized_128x128_8x2_nhwc ... $ make cutlass_profiler -j16"><pre>$ cmake .. -DCUTLASS_NVCC_ARCHS=<span class="pl-s"><span class="pl-pds">'</span>75;80<span class="pl-pds">'</span></span> -DCUTLASS_LIBRARY_KERNELS=cutlass_simt_sfprop_optimized_128x128_8x2_nhwc ... $ make cutlass_profiler -j16</pre></div> <p dir="auto">Example command line for profiling one CUDA Core convolution kernel:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="$ ./tools/profiler/cutlass_profiler --kernels=cutlass_simt_sfprop_optimized_128x128_8x2_nhwc --n=8 --h=224 --w=224 --c=128 --k=128 --r=3 --s=3 ============================= Problem ID: 1 Provider: CUTLASS OperationKind: conv2d Operation: cutlass_simt_sfprop_optimized_128x128_8x2_nhwc Status: Success Verification: ON Disposition: Passed reference_device: Passed Arguments: --conv_kind=fprop --n=8 --h=224 --w=224 --c=128 --k=128 --r=3 --s=3 --p=224 --q=224 --pad_h=1 --pad_w=1 \ --stride_h=1 --stride_w=1 --dilation_h=1 --dilation_w=1 --Activation=f32:nhwc --Filter=f32:nhwc --Output=f32:nhwc \ --conv_mode=cross --iterator_algorithm=optimized --alpha=1 --beta=0 --split_k_mode=serial --split_k_slices=1 \ --eq_gemm_provider=none --op_class=simt --accum=f32 --cta_m=128 --cta_n=128 --cta_k=8 --stages=2 --warps_m=4 \ --warps_n=2 --warps_k=1 --inst_m=1 --inst_n=1 --inst_k=1 --min_cc=50 --max_cc=1024 Bytes: 2055798784 bytes FLOPs: 118482796544 flops Runtime: 7.34266 ms Memory: 260.752 GiB/s Math: 16136.2 GFLOP/s ============================= "><pre>$ ./tools/profiler/cutlass_profiler --kernels=cutlass_simt_sfprop_optimized_128x128_8x2_nhwc --n=8 --h=224 --w=224 --c=128 --k=128 --r=3 --s=3 ============================= Problem ID: 1 Provider: CUTLASS OperationKind: conv2d Operation: cutlass_simt_sfprop_optimized_128x128_8x2_nhwc Status: Success Verification: ON Disposition: Passed reference_device: Passed Arguments: --conv_kind=fprop --n=8 --h=224 --w=224 --c=128 --k=128 --r=3 --s=3 --p=224 --q=224 --pad_h=1 --pad_w=1 \ --stride_h=1 --stride_w=1 --dilation_h=1 --dilation_w=1 --Activation=f32:nhwc --Filter=f32:nhwc --Output=f32:nhwc \ --conv_mode=cross --iterator_algorithm=optimized --alpha=1 --beta=0 --split_k_mode=serial --split_k_slices=1 \ --eq_gemm_provider=none --op_class=simt --accum=f32 --cta_m=128 --cta_n=128 --cta_k=8 --stages=2 --warps_m=4 \ --warps_n=2 --warps_k=1 --inst_m=1 --inst_n=1 --inst_k=1 --min_cc=50 --max_cc=1024 Bytes: 2055798784 bytes FLOPs: 118482796544 flops Runtime: 7.34266 ms Memory: 260.752 GiB/s Math: 16136.2 GFLOP/s ============================= </pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">More Details on Compiling CUTLASS Kernels and CUTLASS Profiler</h2><a id="user-content-more-details-on-compiling-cutlass-kernels-and-cutlass-profiler" class="anchor" aria-label="Permalink: More Details on Compiling CUTLASS Kernels and CUTLASS Profiler" href="#more-details-on-compiling-cutlass-kernels-and-cutlass-profiler"><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>Please follow the links for more CMake examples on selectively compiling CUTLASS kernels: <ul dir="auto"> <li><a href="/NVIDIA/cutlass/blob/main/media/docs/quickstart.md#gemm-cmake-examples">GEMM CMake Examples</a></li> <li><a href="/NVIDIA/cutlass/blob/main/media/docs/quickstart.md#convolution-cmake-examples">Implicit GEMM convolution CMake Examples</a></li> </ul> </li> <li><a href="/NVIDIA/cutlass/blob/main/media/docs/profiler.md">Further details about the CUTLASS Profiler are described here.</a></li> </ul> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">About</h1><a id="user-content-about" class="anchor" aria-label="Permalink: About" href="#about"><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">CUTLASS is released by NVIDIA Corporation as Open Source software under the <a href="/NVIDIA/cutlass/blob/main/LICENSE.txt">3-clause "New" BSD license</a>.</p> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">Contributors</h1><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">The official list of CUTLASS developers and contributors is available here: <a href="/NVIDIA/cutlass/blob/main/CONTRIBUTORS.md">CONTRIBUTORS</a>.</p> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">Copyright</h1><a id="user-content-copyright" class="anchor" aria-label="Permalink: Copyright" href="#copyright"><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">Copyright (c) 2017 - 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. SPDX-License-Identifier: BSD-3-Clause</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content=" Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE."><pre class="notranslate"><code> Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. </code></pre></div> </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="cEIoBwB/oYlkF9G026MyWIIzXKPTPfqyiyArPuWKhp/hRNRrsV8MojLMJAuGyRCuMe0dPlGI5bBRYTQLFH0Usw==" /> </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 Templates for Linear Algebra Subroutines </p> <h3 class="sr-only">Topics</h3> <div class="my-3"> <div class="f6"> <a href="/topics/deep-learning" title="Topic: deep-learning" data-view-component="true" class="topic-tag topic-tag-link"> deep-learning </a> <a href="/topics/cpp" title="Topic: cpp" data-view-component="true" class="topic-tag topic-tag-link"> cpp </a> <a href="/topics/gpu" title="Topic: gpu" data-view-component="true" class="topic-tag topic-tag-link"> gpu </a> <a href="/topics/cuda" title="Topic: cuda" data-view-component="true" class="topic-tag topic-tag-link"> cuda </a> <a href="/topics/nvidia" title="Topic: nvidia" data-view-component="true" class="topic-tag topic-tag-link"> nvidia </a> <a href="/topics/deep-learning-library" title="Topic: deep-learning-library" data-view-component="true" class="topic-tag topic-tag-link"> deep-learning-library </a> </div> </div> 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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>111</strong> watching</a> </div> <h3 class="sr-only">Forks</h3> <div class="mt-2"> <a href="/NVIDIA/cutlass/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 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data-pjax="#repo-content-pjax-container" data-turbo-frame="repo-content-turbo-frame" href="/NVIDIA/cutlass/releases/tag/v3.7.0"> <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;">CUTLASS 3.7.0</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="2025-01-18T15:07:29Z" class="no-wrap">Jan 18, 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/cutlass/releases" data-view-component="true" class="Link">+ 34 releases</a></div> </div> </div> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <h2 class="h4 mb-3"> <a href="/orgs/NVIDIA/packages?repo_name=cutlass" data-view-component="true" class="Link--primary no-underline Link d-flex flex-items-center">Packages <span title="0" hidden="hidden" data-view-component="true" class="Counter ml-1">0</span></a></h2> <div class="text-small color-fg-muted" > No packages published <br> </div> </div> </div> <div class="BorderGrid-row" hidden> <div class="BorderGrid-cell"> <include-fragment src="/NVIDIA/cutlass/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/cutlass/graphs/contributors" data-view-component="true" class="Link--primary no-underline Link d-flex flex-items-center">Contributors <span title="164" data-view-component="true" class="Counter ml-1">164</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/hwu36" class="" data-hovercard-type="user" data-hovercard-url="/users/hwu36/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/57973641?s=64&v=4" alt="@hwu36" 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/kerrmudgeon" class="" data-hovercard-type="user" data-hovercard-url="/users/kerrmudgeon/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/11259?s=64&v=4" alt="@kerrmudgeon" 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/ANIKET-SHIVAM" class="" data-hovercard-type="user" data-hovercard-url="/users/ANIKET-SHIVAM/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/3268307?s=64&v=4" alt="@ANIKET-SHIVAM" 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/dumerrill" class="" data-hovercard-type="user" data-hovercard-url="/users/dumerrill/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/1476032?s=64&v=4" alt="@dumerrill" 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/jackkosaian" class="" data-hovercard-type="user" data-hovercard-url="/users/jackkosaian/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/5831848?s=64&v=4" alt="@jackkosaian" 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/reed-lau" class="" data-hovercard-type="user" data-hovercard-url="/users/reed-lau/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/26267436?s=64&v=4" alt="@reed-lau" 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/Peter9606" class="" data-hovercard-type="user" data-hovercard-url="/users/Peter9606/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/2186779?s=64&v=4" alt="@Peter9606" 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/yzhaiustc" class="" data-hovercard-type="user" data-hovercard-url="/users/yzhaiustc/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/43502317?s=64&v=4" alt="@yzhaiustc" 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/manishucsd" class="" data-hovercard-type="user" data-hovercard-url="/users/manishucsd/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/3487276?s=64&v=4" alt="@manishucsd" 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/alihassanijr" class="" data-hovercard-type="user" data-hovercard-url="/users/alihassanijr/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/68103095?s=64&v=4" alt="@alihassanijr" 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/thakkarV" class="" data-hovercard-type="user" data-hovercard-url="/users/thakkarV/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/23328942?s=64&v=4" alt="@thakkarV" 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/danthe3rd" class="" data-hovercard-type="user" data-hovercard-url="/users/danthe3rd/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/43445237?s=64&v=4" alt="@danthe3rd" 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/mnicely" class="" data-hovercard-type="user" data-hovercard-url="/users/mnicely/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/50021634?s=64&v=4" alt="@mnicely" 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/Gregory-Meyer" class="" data-hovercard-type="user" data-hovercard-url="/users/Gregory-Meyer/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/23343587?s=64&v=4" alt="@Gregory-Meyer" 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/cutlass/graphs/contributors" data-view-component="true" class="Link--inTextBlock Link">+ 150 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: 67.1%;" itemprop="keywords" data-view-component="true" class="Progress-item color-bg-success-emphasis"></span> <span style="background-color:#3A4E3A !important;;width: 25.8%;" itemprop="keywords" data-view-component="true" class="Progress-item color-bg-success-emphasis"></span> <span style="background-color:#3572A5 !important;;width: 4.2%;" itemprop="keywords" data-view-component="true" class="Progress-item color-bg-success-emphasis"></span> <span style="background-color:#e34c26 !important;;width: 2.2%;" itemprop="keywords" data-view-component="true" class="Progress-item color-bg-success-emphasis"></span> <span style="background-color:#DA3434 !important;;width: 0.6%;" itemprop="keywords" data-view-component="true" class="Progress-item color-bg-success-emphasis"></span> <span style="background-color:#555555 !important;;width: 0.1%;" 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/cutlass/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>67.1%</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/cutlass/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>25.8%</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/cutlass/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>4.2%</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/cutlass/search?l=html" data-ga-click="Repository, language stats search click, location:repo overview"> <svg style="color:#e34c26;" 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">HTML</span> <span>2.2%</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/cutlass/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>0.6%</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/cutlass/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>0.1%</span> </a> </li> </ul> </div> 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