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...
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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 faulttolerance 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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