Software Speculation on Caching DSMs

Abstract

Clusters with caching DSMs deliver programmability and performance by supporting shared-memory programming model and tolerating communication latency of remote fetches via caching. The input of a data parallel program is partitioned across machines in the cluster while the DSM transparently fetches and caches remote data as needed by the application. Irregular applications are challenging to parallelize because the input related data dependences that manifest at runtime require the use of speculation for efficiently exploiting parallelism. By speculating that there are no cross iteration dependences, multiple iterations of a data parallel loop are executed in parallel using locally cached copies of data; the absence of dependences is validated before committing the speculatively computed results. In this paper we show that in irregular data-parallel applications, while caching helps tolerate long communication latencies, using a value read from the cache in a computation can lead to misspeculation, and thus aggressive caching can degrade performance due to increased misspeculation rate. To limit 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. These optimizations give speedups of 2.24${\times }$ for graph coloring, 1.71${\times }$ for connected components, 1.88${\times }$ for community detection, 1.32${\times }$ for shortest path, and 1.74${\times }$ for pagerank over baseline parallel executions.