Efficient categorization of memory sharing patterns in software DSM systems

This work introduces a new technique that enables SDSMs to categorize dynamically and accurately memory sharing patterns in both classes of regular and irregular applications. The categorization is carried out automatically at run-time on a per-page basis, requiring no user or compiler assistance. We evaluate the potential benefits of our technique using execution-driven simulations of 8 applications running on TrendMarks on a network of 8 workstations. Surprisingly, we found that producer-consumer(s) and migratory are the dominant patterns even in irregular applications. Preliminary results suggest that the categorization technique we propose is a promising option to further improve the performance of current adaptive SDSM systems.