We developed a Performance Modeling Tools (PMTOOLS)library to enable simulation-based performance modeling for parallel sparse linear algebra algorithms. The library includes micro-benchmarks for calibrating the system's parameters, functions for collecting and retrieving performance data, and a cache simulator for modeling the detailed memory system activities. Using these tools, we have built simulation modules to model and predict performance of different variants of parallel sparse LU and Cholesky factorization algorithms. We validated the simulated results with the existing implementation in SuperLU_DIST, and showed that our performance prediction errors are only 6.1% and 6.6% with 64 processors IBM power5 and Cray XT4, respectively. More importantly, we have successfully used this simulation framework to forecast the performance of different algorithm choices, and helped prototyping new algorithm implementations.
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