IRCS Technical Reports Series
Document Type
Technical Report
Date of this Version
December 1994
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.
Date Posted: 19 September 2006
Comments
University of Pennsylvania Institute for Research in Cognitive Science Technical Report No. IRCS-94-27.