Evaluating the communication performance of MPPs using synthetic sparse matrix multiplication workloads

Abstract

Communication has a dominant impact on the performance of massively parallel processors (MPPs). We propose a methodology to evaluate the internode communication performance of MPPs using a controlled set of synthetic workloads. By generating a range of sparse matrices and measuring the performance of a simple parallel algorithm that repeatedly multiplies a sparse matrix by a dense vector, we can determine the relative performance of different communication workloads. Specifiable communication parameters include the number of nodes, the average amount of communication per node, the degree of sharing among the nodes, and the computation-communication ratio. We describe a general procedure for constructing sparse matrices that have these desired communication and computation parameters, and apply a range of these synthetic workloads to evaluate the hierarchical ring interconnection and cache-only memory architecture (COMA) of the Kendall Square Research KSR1 MPP. This analysis discusses the impact of the KSR1 architecture on communication performance, highlighting the utility and impact of the automatic update feature. It also investigates the impact of system contention on the performance, particularly how it causes potential updates to be ignored.