About this scholarly article

Proceedings of the 25th ACM International Symposium on High-Performance Parallel and Distributed Computing - HPDC '16 - New-Sum by Tao, Dingwen; Song, Shuaiwen Leon; Krishnamoorthy, Sriram; Wu, Panruo; Liang, Xin; Zhang, Eddy Z.; Kerbyson, Darren; Chen, Zizhong is a scholarly article available to read on EtoBox.

Emerging high-performance computing platforms, with large component counts and lower power margins, are anticipated to be more susceptible to soft errors in both logic circuits and memory subsystems. We present an online algorithm-based fault tolerance (ABFT) approach to efficiently detect and recover soft errors for general iterative methods. We design a novel checksum-based encoding scheme for matrix-vector multiplication that is resilient to both arithmetic and memory errors. Our design decouples the checksum updating process from the actual computation, and allows adaptive checksum overhead control. Building on this new encoding mechanism, we propose two online ABFT designs that can effectively recover from errors when combined with a checkpoint/rollback scheme. These designs are capable of addressing scenarios under different error rates. Our ABFT approaches apply to a wide range of iterative solvers that primarily rely on matrix-vector multiplication and vector linear operations. We evaluate our designs through comprehensive analytical and empirical analysis. Experimental evaluation on the Stampede supercomputer demonstrates the low performance overheads incurred by our two A

Author

Tao, Dingwen; Song, Shuaiwen Leon; Krishnamoorthy, Sriram; Wu, Panruo; Liang, Xin; Zhang, Eddy Z.; Kerbyson, Darren; Chen, Zizhong

Publisher

ACM Press

Published

2016

Language

EN