Efficient Symbolic Analysis for Parallelizing Compilers and Performance Estimators
The Journal of Supercomputing
Network performance modeling for PVM clusters
Supercomputing '96 Proceedings of the 1996 ACM/IEEE conference on Supercomputing
$P$^$3$$T+$: A performance estimator for distributed and parallel programs
Scientific Programming
Advanced symbolic analysis for compilers: new techniques and algorithms for symbolic program analysis and optimization
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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.