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 ABFT schemes for preconditioned CG (0:4% and 2:2%) and preconditioned BiCGSTAB (1:0% and 4:0%) for the largest SPD matrix from UFL Sparse Matrix Collection. The evaluation also demonstrates the exibility and effectiveness of our proposed designs for detecting and recovering various types of soft errors in general iterative methods.

Meeting Name

25th ACM International Symposium on High-Performance Parallel and Distributed Computing, HPDC '16 (2016: May 31-Jun. 4, Kyoto, Japan)


Computer Science


This work is partially supported by the NSF grants CCF-1305622, ACI-1305624, CCF-1513201, the SZSTI basic research pro- gram JCYJ20150630114942313, and the Special Program for Applied Research on Super Computation of the NSFC- Guangdong Joint Fund (the second phase). This work was also supported in part by the U.S. Department of Energy's (DOE) Office of Science, Office of Advanced Scientific Computing Research, under awards 66905 and 59921.

Keywords and Phrases

Algorithm-Based Fault Tolerance (ABFT); Checkpoint; Checksum; Iterative Methods; Online Error Detection; Resilience; Rollback Recovery; Silent Data Corruption (SDC)

International Standard Book Number (ISBN)


Document Type

Article - Conference proceedings

Document Version

Final Version

File Type





© 2016 Association for Computing Machinery (ACM), All rights reserved.

Publication Date

31 May 2016