ParSy: Inspection and Transformation of Sparse Matrix Computations for Parallelism
| Autor: | Shoaib Kamil, Michelle Mills Strout, Maryam Mehri Dehnavi, Kazem Cheshmi |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Computer science
Locality Parallel algorithm 020207 software engineering 010103 numerical & computational mathematics 02 engineering and technology Parallel computing 01 natural sciences Kernel (linear algebra) Transformation (function) Shared memory Parallel processing (DSP implementation) Synchronization (computer science) 0202 electrical engineering electronic engineering information engineering 0101 mathematics Sparse matrix computations Cholesky decomposition Sparse matrix |
| Zdroj: | SC |
| DOI: | 10.1109/sc.2018.00065 |
| Popis: | In this work, we describe ParSy, a framework that uses a novel inspection strategy along with a simple code transformation to optimize parallel sparse algorithms for shared memory processors. Unlike existing approaches that can suffer from load imbalance and excessive synchronization, ParSy uses a novel task coarsening strategy to create well-balanced tasks that can execute in parallel, while maintaining locality of memory accesses. Code using the ParSy inspector and transformation outperforms existing highly-optimized sparse matrix algorithms such as Cholesky factorization on multi-core processors with speedups of 2.8 x and 3.1 x over the MKL Pardiso and PaStiX libraries respectively. |
| Databáze: | OpenAIRE |
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