The sparse cyclic distribution against its dense counterparts

Abstract

Several methods have been proposed in the literature for the distribution of data on distributed memory machines, either oriented to dense or sparse structures. Many of the real applications, however, deal with both kinds of data jointly. The paper presents techniques for integrating dense and sparse array accesses in a way that optimizes locality and further allows an efficient loop partitioning within a data-parallel compiler. The approach is evaluated through an experimental survey with several compilers and parallel platforms. The results prove the benefits of the BRS sparse distribution when combined with CYCLIC in mixed algorithms and the poor efficiency achieved by well-known distribution schemes when sparse elements arise in the source code.