STATIC PERFORMANCE PREDICTION OF SKELETAL PARALLEL PROGRAMS

Static performance prediction of implicitly parallel functional programs can be facilitated by restricting the source language to be shapely [7]. The resulting analyses should provide valuable support for the calculational style of program derivation. We build upon previous work in the area by extending the range of admissible programs, allowing us to demonstrate the first automated analysis of a complete program derivation, that of the well known maximum segment sum algorithm of Skillicorn and Cai [11]. We examine the accuracy of our predictions against the run time of real parallel programs.

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Automatic performance prediction to support cross development of parallel programs

Proceedings of the SIGMETRICS symposium on Parallel and distributed tools - SPDT '96 ◽

10.1145/238020.238049 ◽

1996 ◽

Cited By ~ 2

Author(s):

Matthias Schumann

Keyword(s):

Performance Prediction ◽

Parallel Programs

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Systematic strategy for performance prediction in improvement of parallel programs

Computer Aided Systems Theory — CAST '94 - Lecture Notes in Computer Science ◽

10.1007/3-540-61478-8_81 ◽

1996 ◽

pp. 252-267 ◽

Cited By ~ 1

Author(s):

Roman Blasko

Keyword(s):

Performance Prediction ◽

Parallel Programs ◽

Systematic Strategy

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SYMBOLIC PERFORMANCE ESTIMATION OF SPECULATIVE PARALLEL PROGRAMS

Parallel Processing Letters ◽

10.1142/s0129626403001471 ◽

2003 ◽

Vol 13 (04) ◽

pp. 513-524 ◽

Cited By ~ 3

Author(s):

H. GAUTAMA ◽

A. J. C. VAN GEMUND

Keyword(s):

Performance Prediction ◽

Execution Time ◽

Time Distribution ◽

Prediction Accuracy ◽

Analytical Approach ◽

Parallel Programs ◽

Performance Estimation ◽

Program Execution ◽

Empirical Distributions ◽

Measurement Results

Speculative parallelism refers to searching in parallel for a solution, such as finding a pattern in a data base, where finding the first solution terminates the whole parallel process. Different performance prediction methods are required as compared to traditional parallelism. In this paper we introduce an analytical approach to predict the execution time distribution of data-dependent parallel programs that feature N-ary and binary speculative parallel compositions. The method is based on the use of statistical moments which allows program execution time distribution to be approximated at O(1) solution complexity. Measurement results for synthetic distributions indicate an accuracy that lies in the percent range while for empirical distributions on internet search engines the prediction accuracy is acceptable, provided sufficient workload unimodality.

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