Using the Run-Time Sizes of Data Structures to Guide Parallel-Thread Creation (1994)
| Venue: | IN PROCEEDINGS OF THE ACM CONFERENCE ON LISP AND FUNCTIONAL PROGRAMMING |
| Citations: | 12 - 2 self |
BibTeX
@INPROCEEDINGS{Huelsbergen94usingthe,
author = {Lorenz Huelsbergen and James R. Larus and Alexander Aiken},
title = {Using the Run-Time Sizes of Data Structures to Guide Parallel-Thread Creation},
booktitle = {IN PROCEEDINGS OF THE ACM CONFERENCE ON LISP AND FUNCTIONAL PROGRAMMING},
year = {1994},
pages = {79--90},
publisher = {ACM Press}
}
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Abstract
Dynamic granularity estimation is a new technique for automatically identifying expressions in functional languages for parallel evaluation. Expressions with little computation relative to thread-creation costs should evaluate sequentially for maximum performance. Static identification of such threads is however difficult. Therefore, dynamic granularity estimation has compile-time and run-time components: Abstract interpretation statically identifies functions whose complexity depends on data structure sizes; the run-time system maintains approximations to these sizes. Compiler-inserted checks consult this size information to make thread creation decisions dynamically. We describe dynamic granularity estimation for a list-based functional language. Extension to general recursive data structures and imperative operations is possible. Performance measurements of dynamic granularity estimation in a parallel ML implementation on a shared-memory machine demonstrate the possibility of large...
