Optimizing irregular shared-memory applications for clusters

  • Authors:

  • Seung-Jai Min;Rudolf Eigenmann

  • Affiliations:

  • Purdue University, West Lafayette, IN, USA;Purdue University, West Lafayette, IN, USA

  • Venue:
  • Proceedings of the 22nd annual international conference on Supercomputing
  • Year:
  • 2008

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Abstract

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.