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is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2503.10855">arXiv:2503.10855</a> <span> [<a href="https://arxiv.org/pdf/2503.10855">pdf</a>, <a href="https://arxiv.org/format/2503.10855">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Programming Languages">cs.PL</span> </div> </div> <p class="title is-5 mathjax"> Hercules: A Compiler for Productive Programming of Heterogeneous Systems </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Arbore%2C+R">Russel Arbore</a>, <a href="/search/cs?searchtype=author&query=Councilman%2C+A">Aaron Councilman</a>, <a href="/search/cs?searchtype=author&query=Routh%2C+X">Xavier Routh</a>, <a href="/search/cs?searchtype=author&query=Ziegler%2C+R">Ryan Ziegler</a>, <a href="/search/cs?searchtype=author&query=Rathi%2C+P">Praneet Rathi</a>, <a href="/search/cs?searchtype=author&query=Adve%2C+V">Vikram Adve</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2503.10855v1-abstract-short" style="display: inline;"> Modern computing systems increasingly rely on composing heterogeneous devices to improve performance and efficiency. Programming these systems is often unproductive: algorithm implementations must be coupled to system-specific logic, including device-specific optimizations, partitioning, and inter-device communication and synchronization, which requires developing different programs for different… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2503.10855v1-abstract-full').style.display = 'inline'; document.getElementById('2503.10855v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2503.10855v1-abstract-full" style="display: none;"> Modern computing systems increasingly rely on composing heterogeneous devices to improve performance and efficiency. Programming these systems is often unproductive: algorithm implementations must be coupled to system-specific logic, including device-specific optimizations, partitioning, and inter-device communication and synchronization, which requires developing different programs for different system configurations. We propose the Juno language, which represents general purpose applications in an imperative form that can be transformed into parallel, optimized, system-specific code using an expressive and granular imperative scheduling language. We also introduce the Hercules compiler, which uses a novel intermediate representation to represent general and device-specific parallel code in a manner that is easy to analyze and manipulate using schedules. Our system achieves competitive performance with hand-optimized device-specific code (geomean speedups of $1.25\times$ and $1.48\times$ on the CPU and GPU) and significantly outperforms a prior general purpose heterogeneous programming system (geomean speedups of $9.31\times$ and $16.18\times$ on the CPU and GPU). <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2503.10855v1-abstract-full').style.display = 'none'; document.getElementById('2503.10855v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 13 March, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.15179">arXiv:2410.15179</a> <span> [<a href="https://arxiv.org/pdf/2410.15179">pdf</a>, <a href="https://arxiv.org/format/2410.15179">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Programming Languages">cs.PL</span> </div> </div> <p class="title is-5 mathjax"> HPVM-HDC: A Heterogeneous Programming System for Accelerating Hyperdimensional Computing </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Arbore%2C+R">Russel Arbore</a>, <a href="/search/cs?searchtype=author&query=Routh%2C+X">Xavier Routh</a>, <a href="/search/cs?searchtype=author&query=Noor%2C+A+R">Abdul Rafae Noor</a>, <a href="/search/cs?searchtype=author&query=Kothari%2C+A">Akash Kothari</a>, <a href="/search/cs?searchtype=author&query=Yang%2C+H">Haichao Yang</a>, <a href="/search/cs?searchtype=author&query=Xu%2C+W">Weihong Xu</a>, <a href="/search/cs?searchtype=author&query=Pinge%2C+S">Sumukh Pinge</a>, <a href="/search/cs?searchtype=author&query=Adve%2C+V">Vikram Adve</a>, <a href="/search/cs?searchtype=author&query=Rosing%2C+T">Tajana Rosing</a>, <a href="/search/cs?searchtype=author&query=Zhou%2C+M">Minxuan Zhou</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.15179v2-abstract-short" style="display: inline;"> Hyperdimensional Computing (HDC), a technique inspired by cognitive models of computation, has been proposed as an efficient and robust alternative basis for machine learning. HDC programs are often manually written in low-level and target specific languages targeting CPUs, GPUs, and FPGAs - these codes cannot be easily retargeted onto HDC-specific accelerators. No previous programming system enab… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.15179v2-abstract-full').style.display = 'inline'; document.getElementById('2410.15179v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.15179v2-abstract-full" style="display: none;"> Hyperdimensional Computing (HDC), a technique inspired by cognitive models of computation, has been proposed as an efficient and robust alternative basis for machine learning. HDC programs are often manually written in low-level and target specific languages targeting CPUs, GPUs, and FPGAs - these codes cannot be easily retargeted onto HDC-specific accelerators. No previous programming system enables productive development of HDC programs and generates efficient code for several hardware targets. We propose a heterogeneous programming system for HDC: a novel programming language, HDC++, for writing applications using a unified programming model, including HDC-specific primitives to improve programmability, and a heterogeneous compiler, HPVM-HDC, that provides an intermediate representation for compiling HDC programs to many hardware targets. We implement two tuning optimizations, automatic binarization and reduction perforation, that exploit the error resilient nature of HDC. Our evaluation shows that HPVM-HDC generates performance-competitive code for CPUs and GPUs, achieving a geomean speed-up of 1.17x over optimized baseline CUDA implementations with a geomean reduction in total lines of code of 1.6x across CPUs and GPUs. Additionally, HPVM-HDC targets an HDC Digital ASIC and an HDC ReRAM accelerator simulator, enabling the first execution of HDC applications on these devices. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.15179v2-abstract-full').style.display = 'none'; document.getElementById('2410.15179v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 December, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 19 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 released 2020-02-24</a>  </span> </div> </div> </main> <footer> <div class="columns is-desktop" role="navigation" 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