A comparison of two model-based performance-prediction techniques for message-passing parallel programs

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Abstract
This paper describes our experience in modeling two significant parallel applications: ARC2D, a 2-dimensional Euler solver; and, Xtrid, a tridiagonal linear solver. Both of these models were expressed in BDL (Behavior Description language) and simulated on an iPSC/860 Hypercube modeled using Axe (Abstract eXecution Environment). BDL models consist of abstract communicating objects: blocks of sequential code are modeled by single RUN statements; all communication operations in the original code are mirrored by corresponding BDL operations in the model. Our ARC2D model was built by first profiling the program to locate the significant loops and then timing the basic blocks within those loops. Simulated completion times were (except in one case) within 8% of measured execution times. Lengthy simulations were necessary for predicting the performance of large-scale runs. For Xtrid, only the loops surrounding communications were modeled; other loops were absorbed into large sequential blocks whose complexity was estimated using statistical regression. This approach yielded a much smaller model whose computation and communication complexities were clearly manifest. Analysis of complexity allowed rapid prediction of large-scale performance without lengthy simulations! Analytically predicted speed-ups were within 7% of those predicted by simulation. Simulated completion times were within 5% of measured execution times. The second approach provides a more effective methodology for simulation-based performance-tuning.

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