%0 Journal Article
%J IEEE Transactions on Computers
%D 2009
%T Highly Scalable Self-Healing Algorithms for High Performance Scientific Computing
%A Zizhong Chen
%A Jack Dongarra
%X As the number of processors in today's high-performance computers continues to grow, the mean-time-to-failure of these computers is becoming significantly shorter than the execution time of many current high-performance computing applications. Although today's architectures are usually robust enough to survive node failures without suffering complete system failure, most of today's high-performance computing applications cannot survive node failures. Therefore, whenever a node fails, all surviving processes on surviving nodes usually have to be aborted and the whole application has to be restarted. In this paper, we present a framework for building self-healing high-performance numerical computing applications so that they can adapt to node or link failures without aborting themselves. The framework is based on FT-MPI and diskless checkpointing. Our diskless checkpointing uses weighted checksum schemes, a variation of Reed-Solomon erasure codes over floating-point numbers. We introduce several scalable encoding strategies into the existing diskless checkpointing and reduce the overhead to survive k failures in p processes from 2[log p]. k ((beta + 2gamma) m + alpha) to (1 + O (radic(p)/radic(m))) 2 . k (beta + 2gamma)m, where alpha is the communication latency, 1/beta is the network bandwidth between processes, {1\over \gamma } is the rate to perform calculations, and m is the size of local checkpoint per process. When additional checkpoint processors are used, the overhead can be reduced to (1 + O (1/radic(m))). k (beta + 2gamma)m, which is independent of the total number of computational processors. The introduced self-healing algorithms are scalable in the sense that the overhead to survive k failures in p processes does not increase as the number of processes p increases. We evaluate the performance overhead of our self-healing approach by using a preconditioned conjugate gradient equation solver as an example.
%B IEEE Transactions on Computers
%V 58
%P 1512-1524
%8 2009-11
%G eng
%N 11
%R https://doi.org/10.1109/TC.2009.42