›› 2012,Vol. ›› Issue (2): 240-255.doi: 10.1007/s11390-012-1220-5

所属专题: Computer Architecture and Systems

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PartialRC:一种针对GPGPU高效故障恢复的部分复算方法

Xin-Hai Xu1 (徐新海), Student Member, CCF, ACM Xue-Jun Yang1 (杨学军), Senior Member, CCF, Member, ACM, IEEE Jing-Ling Xue2 (薛京灵), Senior Member, IEEE, Member, ACM Yu-Fei Lin1 (林宇斐), Student Member, CCF, ACM, and Yi-Song Lin1 (林一松)   

  • 收稿日期:2011-06-13 修回日期:2012-01-06 出版日期:2012-03-05 发布日期:2012-03-05

PartialRC: A Partial Recomputing Method for Efficient Fault Recovery on GPGPUs

Xin-Hai Xu1 (徐新海), Student Member, CCF, ACM Xue-Jun Yang1 (杨学军), Senior Member, CCF, Member, ACM, IEEE Jing-Ling Xue2 (薛京灵), Senior Member, IEEE, Member, ACM Yu-Fei Lin1 (林宇斐), Student Member, CCF, ACM, and Yi-Song Lin1 (林一松)   

  1. 1. National Laboratory for Parallel and Distributed Processing, School of Computer, National University of Defense Technology, Changsha 410073, China;
    2. Programming Languages and Compilers Group, School of Computer Science and Engineering University of New South Wales, Sydney, Australia
  • Received:2011-06-13 Revised:2012-01-06 Online:2012-03-05 Published:2012-03-05
  • Supported by:

    This work was supported by the National Natural Science Foundation of China under Grant Nos. 60921062, 61003087, 61120106005 and 61170049.

在由CPU和GPU构成的计算机系统中,GPGPU越来越多的被用作高性能计算应用的加速器,比如去年国防科学技术大学制造的,在当时Top500榜单中排名第一的天河-1A系统。但是,尽管拥有性能优势,GPGPU并不提供内置用于提高可靠性的容错方法,而这正是高性能计算应用所必需的。通过分析程序在GPGPU上运行时的SIMT特性,我们开发了一种新的基于检查点编译指导的部分复算方法——PartialRC,通过利用GPGPU超高性能,实现高效的故障恢复。在本文中,我们提出了我们的PartialRC,它可以在检测出一段代码的计算错误后,针对该段代码进行部分复算;描述了基于PartialRC的基于检查点的容错框架;并讨论了其在CUDA平台上的实现。通过在NVIDIA GPGPU上对一些典型CUDA程序的测试,与FullPC(一种传统的针对CPU的基于检查点-回滚-重启的故障恢复方法)相比,PartialRC可以显著降低故障恢复的开销:对于发生较早和较晚的两类错误分别平均降低73.5%和74.6%。另外,在不增加无故障性能开销的同时,PartialRC还降低了由于全部复算所引起的错误检测开销。

Abstract: GPGPUs are increasingly being used to as performance accelerators for HPC (High Performance Computing) applications in CPU/GPU heterogeneous computing systems, including TianHe-1A, the world's fastest supercomputer in the TOP500 list, built at NUDT (National University of Defense Technology) last year. However, despite their performance advantages, GPGPUs do not provide built-in fault-tolerant mechanisms to offer reliability guarantees required by many HPC applications. By analyzing the SIMT (single-instruction, multiple-thread) characteristics of programs running on GPGPUs, we have developed PartialRC, a new checkpoint-based compiler-directed partial recomputing method, for achieving efficient fault recovery by leveraging the phenomenal computing power of GPGPUs. In this paper, we introduce our PartialRC method that recovers from errors detected in a code region by partially re-computing the region, describe a checkpoint-based fault-tolerance framework developed on PartialRC, and discuss an implementation on the CUDA platform. Validation using a range of representative CUDA programs on NVIDIA GPGPUs against FullRC (a traditional full-recomputing Checkpoint-Rollback-Restart fault recovery method for CPUs) shows that PartialRC reduces significantly the fault recovery overheads incurred by FullRC, by 73.5% when errors occur earlier during execution and 74.6% when errors occur later on average. In addition, PartialRC also reduces error detection overheads incurred by FullRC during fault recovery while incurring negligible performance overheads when no fault happens.

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