Online Algorithm-Based Fault Tolerance for Cholesky Decomposition on Heterogeneous Systems with GPUs

Abstract

Extensive researches have been done on developing and optimizing algorithm-based fault tolerance (ABFT) schemes for systolic arrays and general purpose microprocessors. However, little has been done on developing and optimizing ABFT schemes for heterogeneous systems with GPU accelerators. While existing ABFT schemes can correct computing errors like 1+1=3, we find that many memory storage errors can not be corrected by existing ABFT schemes. In this paper, we first develop a new ABFT scheme for Cholesky decomposition that can correct both computing errors and storage errors at the same time, and then develop several optimization techniques to reduce the fault tolerance overhead of ABFT for heterogeneous systems with GPU accelerators. Experimental results demonstrate that our fault tolerant Cholesky decomposition is able to correct both computing errors and storage errors in the middle of the computation and can achieve better performance than the state-of-the-art vendor provided version Cholesky decomposition library routine in CULA R18.

Meeting Name

30th International Parallel and Distributed Processing Symposium, IPDPS 2016 (2016: May 23-27, Chicago, IL)

Department(s)

Computer Science

Comments

This work is partially supported by the NSF grants CCF-1305622, ACI-1305624, CCF-1513201, and the SZSTI basic research program JCYJ20150630114942313.

Keywords and Phrases

Cholesky Decomposition; CULA; Fault Tolerance; GPUs; MAGMA; Offline ABFT; Online ABFT

International Standard Book Number (ISBN)

978-150902140-6

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2016 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

18 Jul 2016

Link to Full Text

Share

 
COinS