Data parallel scheduling of Operations in Linear Algebra on heterogeneous clusters

The aim of data and task parallel scheduling for dense linear algebra kernels is to minimize the processing time of an application composed by several linear algebra ker-nels. The scheduling strategy presented here combines the task parallelism used when scheduling independent tasks and the data parallelism used for linear algebra kernels. This problem has been studied for scheduling indepen-dent tasks on homogeneous machines. Here it is proposed a methodology for heterogeneous clusters and it is shown that significant improvements can be achieved with this strategy