Supporting Realistic OpenMP Applications on a Commodity Cluster of Workstations

This paper describes compiler techniques that can translate standard OpenMP applications into code for distributed computer systems and optimization techniques to enhance the performance of realistic OpenMP application. OpenMP has emerged as an important model and language extension for shared-memory parallel programming. However, despite OpenMP's success on these platforms, it is not currently being used on distributed system. The goal of our project is to quantify the degree to which such a use is possible and develop supporting compiler techniques. Our present compiler techniques translate OpenMP programs into a form suitable for execution on a Software DSM system. We have implemented a compiler that performs this basic translation, and we have studied optimization techniques that improve the baseline performance of OpenMP applications on distributed computer systems. Our results show that, while kernel benchmarks can show high eÆciency of OpenMP programs on distributed systems, full applications need careful consideration of shared data access patterns. A naive translation (similar to OpenMP compilers for SMPs) leads to acceptable performance in very few applications only. However, additional optimizations, including computation repartitioning and access privatization result in average 300% (or from 20% to x16) speedup improvement on our SPEC OMPM2001 benchmarks.