为高性能计算和人工智能设计高性能和可扩展中间件的挑战和机遇:过去,现在和未来

D. Panda
{"title":"为高性能计算和人工智能设计高性能和可扩展中间件的挑战和机遇:过去,现在和未来","authors":"D. Panda","doi":"10.1109/ipdps53621.2022.00009","DOIUrl":null,"url":null,"abstract":"This talk focuses on challenges and opportunities emerging over the years (past, present, and future) in designing middleware for HPC and AI (Deep/Machine Learning) workloads on modern high-end computing systems. The talk initially presents the challenges in designing HPC runtime environments with MPI+X programming models by considering support for dense multi-core CPUs, high-performance interconnects, GPUs, and emerging DPUs. Advanced designs and solutions (such as RDMA, in-network computing, GPUDirect RDMA, on-the-fly compression) to exploit novel features of these emerging technologies and their benefits in the context of MVAPICH2 libraries are presented. Next, the talk focuses on MPI-driven solutions for the Deep/Machine Learning domains to extract performance and scalability for popular Deep Learning frameworks, large out-of-core models, GPUs, and DPUs. MPI-driven solutions to accelerate data science applications like Dask are highlighted. Challenges and experiences in deploying this middleware to the HPC cloud environments for Azure, AWS, and Oracle Cloud are presented. The talk concludes with an overview of the newly established NSF-AI Institute ICICLE (https://icicle.osu.edu/) to address challenges in designing future high-performance edge-to-HPC/ cloud middleware for AI-driven data-intensive applications.","PeriodicalId":321801,"journal":{"name":"2022 IEEE International Parallel and Distributed Processing Symposium (IPDPS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Challenges and Opportunities in Designing High-Performance and Scalable Middleware for HPC and AI: Past, Present, and Future\",\"authors\":\"D. Panda\",\"doi\":\"10.1109/ipdps53621.2022.00009\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This talk focuses on challenges and opportunities emerging over the years (past, present, and future) in designing middleware for HPC and AI (Deep/Machine Learning) workloads on modern high-end computing systems. The talk initially presents the challenges in designing HPC runtime environments with MPI+X programming models by considering support for dense multi-core CPUs, high-performance interconnects, GPUs, and emerging DPUs. Advanced designs and solutions (such as RDMA, in-network computing, GPUDirect RDMA, on-the-fly compression) to exploit novel features of these emerging technologies and their benefits in the context of MVAPICH2 libraries are presented. Next, the talk focuses on MPI-driven solutions for the Deep/Machine Learning domains to extract performance and scalability for popular Deep Learning frameworks, large out-of-core models, GPUs, and DPUs. MPI-driven solutions to accelerate data science applications like Dask are highlighted. Challenges and experiences in deploying this middleware to the HPC cloud environments for Azure, AWS, and Oracle Cloud are presented. The talk concludes with an overview of the newly established NSF-AI Institute ICICLE (https://icicle.osu.edu/) to address challenges in designing future high-performance edge-to-HPC/ cloud middleware for AI-driven data-intensive applications.\",\"PeriodicalId\":321801,\"journal\":{\"name\":\"2022 IEEE International Parallel and Distributed Processing Symposium (IPDPS)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE International Parallel and Distributed Processing Symposium (IPDPS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ipdps53621.2022.00009\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE International Parallel and Distributed Processing Symposium (IPDPS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ipdps53621.2022.00009","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

这次演讲的重点是在现代高端计算系统上为高性能计算和人工智能(深度/机器学习)工作负载设计中间件方面出现的挑战和机遇(过去、现在和未来)。该演讲首先介绍了在使用MPI+X编程模型设计HPC运行时环境时所面临的挑战,包括考虑对密集多核cpu、高性能互连、gpu和新兴dpu的支持。介绍了先进的设计和解决方案(如RDMA、网络内计算、GPUDirect RDMA、动态压缩),以利用这些新兴技术的新特性及其在MVAPICH2库环境中的优势。接下来,演讲重点关注深度/机器学习领域的mpi驱动解决方案,以提取流行的深度学习框架,大型外核模型,gpu和dpu的性能和可扩展性。mpi驱动的解决方案可以加速数据科学应用,如Dask。介绍了在Azure、AWS和Oracle cloud的HPC云环境中部署该中间件的挑战和经验。讲座最后概述了新成立的NSF-AI研究所ICICLE (https://icicle.osu.edu/),该研究所旨在解决为ai驱动的数据密集型应用设计未来高性能边缘到高性能计算/云中间件的挑战。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Challenges and Opportunities in Designing High-Performance and Scalable Middleware for HPC and AI: Past, Present, and Future
This talk focuses on challenges and opportunities emerging over the years (past, present, and future) in designing middleware for HPC and AI (Deep/Machine Learning) workloads on modern high-end computing systems. The talk initially presents the challenges in designing HPC runtime environments with MPI+X programming models by considering support for dense multi-core CPUs, high-performance interconnects, GPUs, and emerging DPUs. Advanced designs and solutions (such as RDMA, in-network computing, GPUDirect RDMA, on-the-fly compression) to exploit novel features of these emerging technologies and their benefits in the context of MVAPICH2 libraries are presented. Next, the talk focuses on MPI-driven solutions for the Deep/Machine Learning domains to extract performance and scalability for popular Deep Learning frameworks, large out-of-core models, GPUs, and DPUs. MPI-driven solutions to accelerate data science applications like Dask are highlighted. Challenges and experiences in deploying this middleware to the HPC cloud environments for Azure, AWS, and Oracle Cloud are presented. The talk concludes with an overview of the newly established NSF-AI Institute ICICLE (https://icicle.osu.edu/) to address challenges in designing future high-performance edge-to-HPC/ cloud middleware for AI-driven data-intensive applications.
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
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信