For many scientific applications, dense matrix multiplication is one of the most important and computation intensive linear algebra operations. An efficient matrix multiplication on high performance and parallel computers requires optimizations on how matrices are decomposed and exchanged between com- putational nodes to reduce communication and synchronization overhead, as well as to efficiently exploit the memory hierarchy within a node to improve both spatial and temporal data locality. In this paper, we presented our studies of performance, cache behavior, and energy efficiency of multiple parallel matrix multiplication algorithms on a multicore desktop computer and a medium-size shared memory machine, both being considered as referenced sizes of nodes to create a medium- and large- scale computational clusters for high performance computing used in industry and national laboratories. Our results highlight both the performance and energy efficiencies, and also provide implications on the memory and resources pressures of those algorithms. We hope this could help users choose the appropriate implementations according to their specific data sets when composing larger-scale scientific applications that use parallel matrix multiplication kernels on a node.
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