Cluster Computing and the Grid, IEEE International Symposium on (2010)
Melbourne, VIC, Australia
May 17, 2010 to May 20, 2010
Although shared memory programming models show good programmability compared to message passing programming models, their implementation by page-based software distributed shared memory systems usually suffers from high memory consistency costs. The major part of these costs is inter-node data transfer for keeping virtual shared memory consistent. A good prefetch strategy can reduce this cost. We develop two prefetch techniques, TReP and HReP, which are based on the execution history of each parallel region. These techniques are evaluated using offline simulations with the NAS Parallel Benchmarks and the LINPACK benchmark. On average, TReP achieves an efficiency (ratio of pages prefetched that were subsequently accessed) of 96% and a coverage (ratio of access faults avoided by prefetches) of 65%. HReP achieves an efficiency of 91% but has a coverage of 79%. Treating the cost of an incorrectly prefetched page to be equivalent to that of a miss, these techniques have an effective page miss rate of 63% and 71% respectively. Additionally, these two techniques are compared with two well-known software distributed shared memory (sDSM) prefetch techniques, Adaptive++ and TODFCM. TReP effectively reduces page miss rate by 53% and 34% more, and HReP effectively reduces page miss rate by 62% and 43% more, compared to Adaptive++ and TODFCM respectively. As for Adaptive++, these techniques also permit bulk prefetching for pages predicted using temporal locality, amortizing network communication costs and permitting bandwidth improvement from multi-rail network interfaces.
Parallel regions, Software distributed shared memory systems, Prefetch techniques
Jie Cai, Peter E. Strazdins, Alistair P. Rendell, "Region-Based Prefetch Techniques for Software Distributed Shared Memory Systems", Cluster Computing and the Grid, IEEE International Symposium on, vol. 00, no. , pp. 113-122, 2010, doi:10.1109/CCGRID.2010.16