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

Content Provider	Springer Nature Link
Author	Xu, Xin Hai Yang, Xue Jun Xue, Jing Ling Lin, Yu Fei Lin, Yi Song
Copyright Year	2012
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.
Starting Page	240
Ending Page	255
Page Count	16
File Format	PDF
ISSN	10009000
Journal	Journal of Computer Science and Technology
Volume Number	27
Issue Number	2
e-ISSN	18604749
Language	English
Publisher	Springer US
Publisher Date	2012-03-05
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	GPGPU partial recomputing fault tolerance CUDA checkpointing Software Engineering Computer Science Data Structures, Cryptology and Information Theory Artificial Intelligence (incl. Robotics) Information Systems Applications (incl. Internet) Theory of Computation
Content Type	Text
Resource Type	Article
Subject	Theoretical Computer Science Computational Theory and Mathematics Computer Science Applications Software Hardware and Architecture

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A Survey on Partial Retrieval of 3D Shapes

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

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