Processes Distribution of Homogeneous Parallel Linear Algebra Routines on Heterogeneous Clusters

This paper presents a self-optimization methodology for parallel linear algebra rou-tines on heterogeneous systems. For each routine, a series of decisions is taken automatically in order to obtain an execution time close to the optimum (without rewriting the routine’s code). Some of these decisions are: the number of processes to generate, the heterogeneous distribution of these processes over the network of processors, the logical topology of the generated processes,... To reduce the search-ing space of such decisions, different heuristics have been used. The experiments have been performed with a parallel LU factorization routine similar to the ScaLAPACK one, and good results have been obtained on different heterogeneous platforms