Processes Distribution of Homogeneous Parallel Linear 
Algebra Routines on Heterogeneous Clusters
Jacier Cuenca, Luis Pedro Garcia, Domingo Gimenez, Jack Dongarra

This paper presents a self-optimization methodology for parallel linear algebra
routines 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 searching 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.