Algorithm-based fault tolerance applied to high performance computing

We present a new approach to fault tolerance for High Performance Computing system. Our approach is based on a careful adaptation of the Algorithm-Based Fault Tolerance technique [K. Huang, J. Abraham, Algorithm-based fault tolerance for matrix operations, IEEE Transactions on Computers (Spec. Issue...

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Bibliographic Details
Published in:Journal of parallel and distributed computing Vol. 69; no. 4; pp. 410 - 416
Main Authors: Bosilca, George, Delmas, Rémi, Dongarra, Jack, Langou, Julien
Format: Journal Article
Language:English
Published: Amsterdam Elsevier Inc 01.04.2009
Elsevier
Subjects:
Algebra
Applied sciences
Computer science; control theory; systems
Computer systems and distributed systems. User interface
Computer systems performance. Reliability
Exact sciences and technology
Fault tolerance
High performance computing
Linear algebra
Linear and multilinear algebra, matrix theory
Mathematics
Sciences and techniques of general use
Software
High performance computing
Fault tolerance
Linear algebra
High performance
On the fly
Parallel algorithm
Subroutine
Rupture
Matrix product
Parallel
Transaction processing
Distributed computing
Modeling
Matrix calculus
Parallel computation
Error correction
Viability
Fault tolerant system
ISSN:0743-7315, 1096-0848
Online Access:Get full text
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Summary:We present a new approach to fault tolerance for High Performance Computing system. Our approach is based on a careful adaptation of the Algorithm-Based Fault Tolerance technique [K. Huang, J. Abraham, Algorithm-based fault tolerance for matrix operations, IEEE Transactions on Computers (Spec. Issue Reliable & Fault-Tolerant Comp.) 33 (1984) 518–528] to the need of parallel distributed computation. We obtain a strongly scalable mechanism for fault tolerance. We can also detect and correct errors (bit-flip) on the fly of a computation. To assess the viability of our approach, we have developed a fault-tolerant matrix–matrix multiplication subroutine and we propose some models to predict its running time. Our parallel fault-tolerant matrix–matrix multiplication scores 1.4 TFLOPS on 484 processors (cluster jacquard.nersc.gov) and returns a correct result while one process failure has happened. This represents 65% of the machine peak efficiency and less than 12% overhead with respect to the fastest failure-free implementation. We predict (and have observed) that, as we increase the processor count, the overhead of the fault tolerance drops significantly.
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ISSN:0743-7315
1096-0848
DOI:10.1016/j.jpdc.2008.12.002