A Performance Prediction Framework for Data Intensive Applications on Large Scale Parallel Machines
LCR '98 Selected Papers from the 4th International Workshop on Languages, Compilers, and Run-Time Systems for Scalable Computers
Performance and modularity benefits of message-driven execution
Journal of Parallel and Distributed Computing
Efficient Performance Prediction for Large-Scale, Data-Intensive Applications
International Journal of High Performance Computing Applications
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Abstract: Simulation studies are quite useful for performance prediction on new architectures and for systematic analysis of performance perturbations caused by variations in the machine parameters, such as communication latencies. Trace-driven simulation is necessary to avoid large computational costs over multiple simulation runs. However, trace-driven simulation of nondeterministic programs has turned out to be almost impossible. Simulation of message-driven programs is particularly changing in this context because they are inherently nondeterministic. Yet message-driven execution is a very effective technique for enhancing performance, particularly in the presence of large or unpredictable communication latencies. We present a methodology for simulating message-driven programs. The information that is necessary to carry out such simulations is identified, and a method for extracting such information from program executions is described.