Computation and Data Partitioning on Scalable Shared Memory Multiprocessors

In this paper we identify the factors that affect the derivation of computation and data partitions on scalable shared memory multiprocessors (SSMMs). We show that these factors necessitate an SSMM-conscious approach. In addition to remote memory access, which is the sole factor on distributed memory multiprocessors, cache affinity, memory contention and false sharing are important factors that must be considered. Experimental evidence is presented to demonstrate the impact of these factors on performance using three applications on the KSR1 and the Hector multiprocessors.