Enhancing the performance of autoscheduling in Distributed Shared Memory multiprocessors

Abstract

Autoscheduling is a parallel program compilation and execution model that combines uniquely three features: Automatic extraction of loop and functional parallelism at any level of granularity, dynamic scheduling of parallel tasks, and dynamic program adaptability on multiprogrammed shared memory multiprocessors. This paper presents a technique that enhances the performance of autoscheduling in Distributed Shared Memory (DSM) multiprocessors, targetting mainly at medium and large scale systems, where poor data locality and excessive communication impose performance bottlenecks. Our technique partitions the application Hierarchical Task Graph and maps the derived partitions to clusters of processors in the DSM architecture. Autoscheduling is then applied separately for each partition to enhance data locality and reduce communication costs. Our experimental results show that partitioning achieves remarkable performance improvements compared to a standard autoscheduling environment and a commercial parallelizing compiler.KeywordsTask GraphRuntime SystemDistribute Shared MemoryDynamic Program AdaptabilitySynthetic BenchmarkThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.