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We present a new approach to fault tolerance for High Performance Computingsystem. Our approach is based on a careful adaptation of the Algorithmic BasedFault Tolerance technique (Huang and Abraham, 1984) to the need of paralleldistributed computation. We obtain a strongly scalable mechanism for faulttolerance. We can also detect and correct errors (bit-flip) on the fly of acomputation. To assess the viability of our approach, we have developed a faulttolerant matrix-matrix multiplication subroutine and we propose some models topredict its running time. Our parallel fault-tolerant matrix-matrixmultiplication scores 1.4 TFLOPS on 484 processors (cluster jacquard.nersc.gov)and returns a correct result while one process failure has happened. Thisrepresents 65% of the machine peak efficiency and less than 12% overhead withrespect to the fastest failure-free implementation. We predict (and haveobserved) that, as we increase the processor count, the overhead of the faulttolerance drops significantly.

Algorithmic Based Fault Tolerance Applied to High Performance Computing