Abstract

As processors continue to boost the system performance with higher circuit density, shrinking process technology and near-threshold voltage (NTV) operations, they are projected to be more vulnerable to transient faults, which have become one of the major concerns for future extreme-scale HPC systems. Despite being relatively infrequent, crashes due to transient faults are incredibly disruptive, particularly for massively parallel jobs on supercomputers where they potentially kill the entire job, requiring an expensive rerun or restart from a checkpoint. In this paper, we present CARE, a light-weight compiler-assisted technique to repair the (crashed) process on-the-fly when a crash-causing error is detected, allowing applications to continue their executions instead of being simply terminated and restarted. Specifically, CARE seeks to repair failures that would result in application crashes due to invalid memory references (segmentation violation). During the compilation of applications, CARE constructs a recovery kernel for each crash-prone instruction, and upon an occurrence of an error, CARE attempts to repair corrupted state of the process by executing the constructed recovery kernel to recompute the memory reference on-the-fly. We evaluated CARE with four scientific workloads. During their normal execution, CARE incurs almost zero runtime overhead and a fixed 27MB memory overheads. Meanwhile, CARE can recover on an average 83.54% of crash-causing errors within dozens of milliseconds. We also evaluated CARE with parallel jobs running on 3072 cores and showed that CARE can successfully mask the impact of crash-causing errors by providing almost uninterrupted execution. Finally, We present our preliminary evaluation result for BLAS, which shows that CARE is capable of recovering failures in libraries with a very high coverage rate of 83% and negligible overheads. With such an effective recovery mechanism, CARE could tremendously mitigate the overheads and resource requirements of the resilience subsystem in future extreme-scale systems.

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