Predicting remote reuse distance patterns in UPC applications

Current work in high productivity parallel computing has focused attention on the class of partitioned global address space (PGAS) parallel programming languages because they promise to reduce the effort required to develop parallel application codes. An important aspect in achieving good performance in PGAS languages is effective handling of remote memory references. We extend a single-threaded reuse distance model to predict memory behavior for multi-threaded UPC applications. Our model handles changes in per-thread data size as well as changes in thread mapping due to problem size increases. Our results indicate the model provides good predictions of remote memory behavior by accurately predicting changes in remote memory reuse distance as a function of the problem size and the number of threads.