Exploiting the locality properties of Peano curves for parallel matrix multiplication

The present work studies an approach to exploit the locality properties of an inherently cache-efficient algorithm for matrix multiplication in a parallel implementation. The algorithm is based on a blockwise element layout and an execution order that are derived from a Peano space-filling curve. The strong locality properties induced in the resulting algorithm motivate a parallel algorithm that replicates matrix blocks in local caches that will prefetch remote blocks before they are used. As a consequence, the block size for matrix multiplication and the cache sizes, and hence the granularity of communication, can be chosen independently. The influence of these parameters on parallel efficiency is studied on a compute cluster with 128 processors. Performance studies show that the largest influence on performance stems from the size of the local caches, which makes the algorithm an interesting option for all situations where memory is scarce, or where existing cache hierarchies can be exploited (as in future manycore environments, e.g.).