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Author Clement, Mark J. ♦ Quinn, Michael J.
Source CiteSeerX
Content type Text
File Format PDF
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Large Computational Problem ♦ Available Number ♦ Eventual Execution ♦ Problem Size ♦ Massively Parallel Processing ♦ Different Hardware Architecture ♦ Symbolic Analysis ♦ Performance Tuning ♦ Program Source Code ♦ Parallel Computer ♦ Analytical Model ♦ Performance Prediction Methodology ♦ Scalable Parallel Program ♦ Symbolic Performance Prediction ♦ Recent Advance ♦ Algebraic Manipulation
Description Recent advances in the power of parallel computers have made them attractive for solving large computational problems. Scalable parallel programs are particularly well suited to Massively Parallel Processing (MPP) machines since the number of computations can be increased to match the available number of processors. Performance tuning can be particularly difficult for these applications since it must often be performed with a smaller problem size than that targeted for eventual execution. This research develops a performance prediction methodology that addresses this problem through symbolic analysis of program source code. Algebraic manipulations can then be performed on the resulting analytical model to determine performance for scaled up applications on different hardware architectures.
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article
Publisher Date 1995-01-01
Publisher Institution IN PROC. OF 9TH INTERNATIONAL PARALLEL PROCESSING SYMPOSIUM