GPU多进程服务安全退出进程

Hao Wu, Wei Liu, Yifan Gong, Jiangming Jin
{"title":"GPU多进程服务安全退出进程","authors":"Hao Wu, Wei Liu, Yifan Gong, Jiangming Jin","doi":"10.1109/ICDCS47774.2020.00125","DOIUrl":null,"url":null,"abstract":"GPUs have been widely adopted to speedup various throughput-originated applications running on HPC platforms, where typically there are a number of tasks sharing GPUs to maximize GPU utilization. To facilitate GPU sharing, GPU vendors provide tools, allowing multiple processes concurrently to use GPUs. For example, Nvidia provides MPS (Multi-Process Service) managing all GPU processes to achieve high throughput by fully exploiting hardware resources. However, such tool leads to undesired single point of failure for all GPU processes, namely, one process’s exception makes other processes abnormal. In this work, we investigate the seriousness of this GPU process interferences caused by MPS, and propose an approach to address one of these interferences, which takes place during process quitting. By using signal handling and thread synchronization techniques in this approach, GPU processes are able to quit safely without interfering other GPU processes.","PeriodicalId":158630,"journal":{"name":"2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Safe Process Quitting for GPU Multi-Process Service (MPS)\",\"authors\":\"Hao Wu, Wei Liu, Yifan Gong, Jiangming Jin\",\"doi\":\"10.1109/ICDCS47774.2020.00125\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"GPUs have been widely adopted to speedup various throughput-originated applications running on HPC platforms, where typically there are a number of tasks sharing GPUs to maximize GPU utilization. To facilitate GPU sharing, GPU vendors provide tools, allowing multiple processes concurrently to use GPUs. For example, Nvidia provides MPS (Multi-Process Service) managing all GPU processes to achieve high throughput by fully exploiting hardware resources. However, such tool leads to undesired single point of failure for all GPU processes, namely, one process’s exception makes other processes abnormal. In this work, we investigate the seriousness of this GPU process interferences caused by MPS, and propose an approach to address one of these interferences, which takes place during process quitting. By using signal handling and thread synchronization techniques in this approach, GPU processes are able to quit safely without interfering other GPU processes.\",\"PeriodicalId\":158630,\"journal\":{\"name\":\"2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS)\",\"volume\":\"16 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICDCS47774.2020.00125\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDCS47774.2020.00125","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

GPU已被广泛用于加速运行在HPC平台上的各种吞吐量源应用程序,其中通常有许多任务共享GPU以最大化GPU利用率。为了方便GPU共享,GPU厂商提供了工具,允许多个进程同时使用GPU。例如,Nvidia提供MPS (Multi-Process Service,多进程服务)来管理所有GPU进程,通过充分利用硬件资源来实现高吞吐量。然而,这种工具会导致所有GPU进程的单点故障,即一个进程的异常会导致其他进程异常。在这项工作中,我们研究了由MPS引起的GPU进程干扰的严重性,并提出了一种解决这些干扰的方法,这些干扰发生在进程退出期间。通过在这种方法中使用信号处理和线程同步技术,GPU进程能够在不干扰其他GPU进程的情况下安全退出。
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
Safe Process Quitting for GPU Multi-Process Service (MPS)
GPUs have been widely adopted to speedup various throughput-originated applications running on HPC platforms, where typically there are a number of tasks sharing GPUs to maximize GPU utilization. To facilitate GPU sharing, GPU vendors provide tools, allowing multiple processes concurrently to use GPUs. For example, Nvidia provides MPS (Multi-Process Service) managing all GPU processes to achieve high throughput by fully exploiting hardware resources. However, such tool leads to undesired single point of failure for all GPU processes, namely, one process’s exception makes other processes abnormal. In this work, we investigate the seriousness of this GPU process interferences caused by MPS, and propose an approach to address one of these interferences, which takes place during process quitting. By using signal handling and thread synchronization techniques in this approach, GPU processes are able to quit safely without interfering other GPU processes.
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
An Energy-Efficient Edge Offloading Scheme for UAV-Assisted Internet of Things Kill Two Birds with One Stone: Auto-tuning RocksDB for High Bandwidth and Low Latency BlueFi: Physical-layer Cross-Technology Communication from Bluetooth to WiFi [Title page i] Distributionally Robust Edge Learning with Dirichlet Process Prior
×
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