Abstract

Programming a parallel computer is inherently more complex than programming a sequential computer. This complexity is due to:(1) additional design parameters, such as program partitioning and task to processor mapping, and (2) nonintuitive performance tradeoffs. Because of this complexity, it is inevitable that the initial design of a program will not utilize the available processing resources as effectively as possible. In this dissertation, we investigate methods for understanding and improving the performance of parallel programs. There are two aspects to this work: measurement and presentation. Our approach is to base performance measurement on extending execution profiling to parallel programs. Although profiling has long been recognized as a valuable tool in sequential programming, it's value to parallel programs has not been extensively investigated. In this dissertation we show that there are …

Date

1991

Authors

Carl Kesselman

Institution

University of California, Los Angeles