Optimizing irregular shared-memory applications for clusters

Irregular applications pose challenges in optimizing communication, due to the difficulty of analyzing irregular data accesses accurately and efficiently. This challenge is especially big when translating irregular shared-memory applications to message passing form for clusters. The lack of effective irregular data analysis in the translation system results in unnecessary or redundant communication, which limits application scalability. In this paper, we present a Lean Distributed Shared Memory (LDSM) system, which features a fast and accurate irregular data access (IDA) analysis. The analysis uses a region-based diff method and makes use of a runtime library that is optimized for irregular applications. We describe three optimizations that improve the LDSM system performance. A parallel array reduction transformation reduces overheads in the analysis. A packed communication optimization and a differential communication optimization effectively eliminate unnecessary and redundant messages. We evaluate the performance of the optimized LDSM system on a set of representative irregular benchmarks. The optimized LDSM executes irregular applications on average 45% faster than the hand-tuned MPI applications