A Proposal for a Heterogeneous Cluster ScaLAPACK (Dense Linear Solvers)
The authors study the implementation of dense linear algebra kernels, such as matrix multiplication or linear system solvers, on heterogeneous networks of workstations. The uniform block-cyclic data distribution scheme commonly used for homogeneous collections of processors limits the performance of these linear algebra kernels on heterogeneous grids to the speed of the slowest processor. We present and study more sophisticated data allocation strategies that balance the load on heterogeneous platforms with respect to the performance of the processors. When targeting unidimensional grids, the load-balancing problem can be solved rather easily. When targeting two-dimensional grids, which are the key to scalability and efficiency for numerical kernels, the problem turns out to be surprisingly difficult. We formally state the 2D load-balancing problem and prove its NP-completeness. Next, we introduce a data allocation heuristic, which turns out to be very satisfactory: Its practical usefulness is demonstrated by MPI experiments conducted with a heterogeneous network of workstations.