Optimizing Caching DSM for Distributed Software Speculation

摘要

Clusters with caching DSMs deliver programmability and performance by supporting shared-memory programming and tolerate remote I/O latencies via caching. The input to a data parallel program is partitioned across the cluster while the DSM transparently fetches and caches remote data as needed. Irregular applications, however, are challenging to parallelize because the input related data dependences that manifest at runtime require use of speculation for correct parallel execution. By speculating that there are no input related cross iteration dependences, private copies of the input can be processed by parallelizing the loop, the absence of dependences is validated before committing the computed results. We show that while caching helps tolerate long communication latencies in irregular data-parallel applications, using a cached values in a computation can lead to misspeculation and thus aggressive caching can degrade performance due to increased misspeculation rate. We present optimizations for distributed speculation on caching based DSMs that decrease the cost of misspeculation check and speed up the re-execution of misspeculated recomputations. Optimized distributed speculation achieves speedups of 2.24x for coloring, 1.71x for connected components, 1.88x for community detection, 1.32x for shortest path, and 1.74x for pagerank over unoptimized speculation.