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A Shared-Memory Multiprocessor Implementation of Data-Parallel Operators for ML

Authors
Publisher
ScholarlyCommons
Publication Date
Disciplines
  • Design

Abstract

We have designed and implemented an asynchronous data-parallel scheduler for the SML/NJ ML compiler. Using this general scheduler we built a data-parallel module that provides new operators to manipulate sequences (i.e., arrays, vectors) in parallel. Parallelization concerns such as thread creation and synchronization are hidden from the application programmer by ML's module abstraction. We find that languages with modules, higher-order functions and automatic parallel storage management can, in this manner, seamlessly support data-parallel operators. An implementation of applications using the new sequence module on an eight-processor shared-memory machine indicates that in some cases useful speedup is possible with our approach.

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