Accurate data redistribution cost estimation in software distributed shared memory systems

Abstract

Distributing data is one of the key problems in implementing efficient distributed-memory parallel programs. The problem becomes more difficult in programs where data redistribution between computational phases is considered. The global data distribution problem is to find the optimal distribution in multi-phase parallel programs. Solving this problem requires accurate knowledge of data redistribution cost. We are investigating this problem in the context of a software distributed shared memory (SDSM) system, in which obtaining accurate redistribution cost estimates is difficult. This is because SDSM communication is implicit: It depends on access patterns, page locations, and the SDSM consistency protocol. We have developed integrated compile- and run-time analysis for SDSM systems to determine accurate redistribution cost estimates with low overhead. Our resulting system, SUIF-Adapt, can efficiently and accurately estimate execution time, including redistribution, to within 5% of the actual time in all of our test cases and is often much closer. These precise costs enable SUIF-Adapt to find efficient global data distributions in multiple-phase programs.