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As the number of processors increases to hundreds of thousands in parallel computer architectures, the failure probability rises correspondingly, making fault tolerance a highly important and challenging task. Application-level check-pointing is one of the most popular techniques to proactively deal with unexpected failures because of its portability and flexibility. During the checkpoint phase, the local states of the computation spread across thousands of processors are saved to stable storage. Unfortunately, this approach results in heavy I/O load and can cause an I/O bottleneck in a massively parallel system. In this paper, the authors examine application-level check-pointing for a massively parallel electromagnetic solver system called NekCEM on the IBM Blue Gene/P at Argonne National Laboratory.
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