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Open Access Publications from the University of California

Towards an Accurate Performance Modeling of Parallel Sparse Factorization


We present a performance model to analyze a parallel sparse LU factorization algorithm on modern cached-based, high-end parallel architectures. Our model characterizes the algorithmic behavior bytaking account the underlying processor speed, memory system performance, as well as the interconnect speed. The model is validated using the SuperLU_DIST linear system solver, the sparse matrices from real applications, and an IBM POWER3 parallel machine. Our modeling methodology can be easily adapted to study performance of other types of sparse factorizations, such as Cholesky or QR.

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