SYCL-Bench 2020:在 AMD、Intel 和 NVIDIA GPU 上对 SYCL 2020 进行基准测试

Luigi Crisci, Lorenzo Carpentieri, Peter Thoman, Aksel Alpay, Vincent Heuveline, Biagio Cosenza
{"title":"SYCL-Bench 2020:在 AMD、Intel 和 NVIDIA GPU 上对 SYCL 2020 进行基准测试","authors":"Luigi Crisci, Lorenzo Carpentieri, Peter Thoman, Aksel Alpay, Vincent Heuveline, Biagio Cosenza","doi":"10.1145/3648115.3648120","DOIUrl":null,"url":null,"abstract":"Today, the SYCL standard represents the most advanced programming model for heterogeneous computing, delivering both productivity, portability, and performance in pure C++17. SYCL 2020, in particular, represents a major enhancement that pushes the boundaries of heterogeneous programming by introducing a number of new features. As the new features are implemented by existing compilers, it becomes critical to assess the maturity of the implementation through accurate and specific benchmarking. This paper presents SYCL-Bench 2020, an extended benchmark suite specifically designed to evaluate six key features of SYCL 2020: unified shared memory, reduction kernel, specialization constants, group algorithms, in-order queue, and atomics. We experimentally evaluate SYCL-Bench 2020 on GPUs from the three major vendors, i.e., AMD, Intel, and NVIDIA, and on two different SYCL implementations AdaptiveCPP and oneAPI DPC++.","PeriodicalId":73497,"journal":{"name":"International Workshop on OpenCL","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"SYCL-Bench 2020: Benchmarking SYCL 2020 on AMD, Intel, and NVIDIA GPUs\",\"authors\":\"Luigi Crisci, Lorenzo Carpentieri, Peter Thoman, Aksel Alpay, Vincent Heuveline, Biagio Cosenza\",\"doi\":\"10.1145/3648115.3648120\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Today, the SYCL standard represents the most advanced programming model for heterogeneous computing, delivering both productivity, portability, and performance in pure C++17. SYCL 2020, in particular, represents a major enhancement that pushes the boundaries of heterogeneous programming by introducing a number of new features. As the new features are implemented by existing compilers, it becomes critical to assess the maturity of the implementation through accurate and specific benchmarking. This paper presents SYCL-Bench 2020, an extended benchmark suite specifically designed to evaluate six key features of SYCL 2020: unified shared memory, reduction kernel, specialization constants, group algorithms, in-order queue, and atomics. We experimentally evaluate SYCL-Bench 2020 on GPUs from the three major vendors, i.e., AMD, Intel, and NVIDIA, and on two different SYCL implementations AdaptiveCPP and oneAPI DPC++.\",\"PeriodicalId\":73497,\"journal\":{\"name\":\"International Workshop on OpenCL\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-04-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Workshop on OpenCL\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3648115.3648120\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Workshop on OpenCL","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3648115.3648120","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

如今,SYCL 标准代表了异构计算最先进的编程模型,在纯 C++17 中提供了生产率、可移植性和性能。由于新功能是由现有编译器实现的,因此通过准确而具体的基准测试来评估实现的成熟度变得至关重要。本文介绍了 SYCL-Bench 2020,这是一个扩展的基准测试套件,专门用于评估 SYCL 2020 的六个关键特性:统一共享内存、还原内核、特殊化常量、分组算法、无序队列和原子。我们在三大厂商(即 AMD、Intel 和 NVIDIA)的 GPU 以及两种不同的 SYCL 实现 AdaptiveCPP 和 oneAPI DPC++ 上对 SYCL-Bench 2020 进行了实验性评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
SYCL-Bench 2020: Benchmarking SYCL 2020 on AMD, Intel, and NVIDIA GPUs
Today, the SYCL standard represents the most advanced programming model for heterogeneous computing, delivering both productivity, portability, and performance in pure C++17. SYCL 2020, in particular, represents a major enhancement that pushes the boundaries of heterogeneous programming by introducing a number of new features. As the new features are implemented by existing compilers, it becomes critical to assess the maturity of the implementation through accurate and specific benchmarking. This paper presents SYCL-Bench 2020, an extended benchmark suite specifically designed to evaluate six key features of SYCL 2020: unified shared memory, reduction kernel, specialization constants, group algorithms, in-order queue, and atomics. We experimentally evaluate SYCL-Bench 2020 on GPUs from the three major vendors, i.e., AMD, Intel, and NVIDIA, and on two different SYCL implementations AdaptiveCPP and oneAPI DPC++.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Improving Performance Portability of the Procedurally Generated High Energy Physics Event Generator MadGraph Using SYCL Acceleration of Quantum Transport Simulations with OpenCL CodePin: An Instrumentation-Based Debug Tool of SYCLomatic An Efficient Approach to Resolving Stack Overflow of SYCL Kernel on Intel® CPUs Ray Tracer based lidar simulation using SYCL
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1