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The increase in HPC systems size and complexity, together with increasingon-chip transistor density, power limitations, and number of components, rendermodern HPC systems subject to soft errors. Silent data corruptions (SDCs) aretypically caused by such soft errors in the form of bit-flips in the memorysubsystem and hinder the correctness of scientific applications. This workaddresses the problem of protecting a class of iterative computational kernels,called stencils, against SDCs when executing on parallel HPC systems. ExistingSDC detection and correction methods are in general either inaccurate,inefficient, or targeting specific application classes that do not includestencils. This work proposes a novel algorithm-based fault tolerance (ABFT)method to protect scientific applications that contain arbitrary stencilcomputations against SDCs. The ABFT method can be applied both online andoffline to accurately detect and correct SDCs in 2D and 3D parallel stencilcomputations. We present a formal model for the proposed method includingtheorems and proofs for the computation of the associated checksums as well aserror detection and correction. We experimentally evaluate the use of theproposed ABFT method on a real 3D stencil-based application (HotSpot3D) via afault-injection, detection, and correction campaign. Results show that theproposed ABFT method achieves less than 8% overhead compared to the performanceof the unprotected stencil application. Moreover, it accurately detects andcorrects SDCs. While the offline ABFT version corrects errors more accurately,it may incur a small additional overhead than its online counterpart.

Algorithm-Based Fault Tolerance for Parallel Stencil Computations