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The Method Of Parallel Recognition And Parallel Optimization Based On Data Dependence With Sparse Matrix

机译:基于数据依赖稀疏矩阵的并行识别和并行优化方法

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Abstract: for application programs in scientific and technological fields have grown increasingly large and complex, it is becoming more difficult to parallelize these programs by hand using message-passing libraries. To reduce this difficulty, we are researching the compilation technology for serial program automatic parallelization. In this paper, the author puts forward a kind of parallel recognition algorithm in parallelization compiler with sparse matrix to reduce memory consumption and time complexity. In the algorithm the author adopts the idea of the medium grain parallel.
机译:摘要:随着科学技术领域中的应用程序变得越来越大和复杂,使用消息传递库手动并行化这些程序变得越来越困难。为了减少这种困难,我们正在研究用于串行程序自动并行化的编译技术。本文提出了一种基于稀疏矩阵的并行编译器并行识别算法,以减少内存消耗和时间复杂度。在算法中,作者采用了中粒并行的思想。

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