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Compiler optimizations for parallel sparse programs with array intrinsics of Fortran 90

机译:具有Fortran 90数组内在函数的并行稀疏程序的编译器优化

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In our recent work, we have been working on providing parallel sparse supports for array intrinsics of Fortran 90. Our supporting library uses a two-level design. In the low-level routines, it requires the input sparse matrices to be specified with compression/distribution schemes for array functions. In the high-level representations, sparse array functions are overloaded with Fortran 90 array intrinsic interfaces so that programmers need not be concerned about low-level details. This raises a very interesting optimization problem in the strategies to transform high-level representations to low-level routines by automatic selections and supplies of distribution and compression schemes for sparse arrays. We propose solutions to this optimization problem. The optimization problem is shown to be NP-hard. We develop a heuristic algorithm based on annotated program graphs, and the algorithm is shown to be practical. Experimental results on an IBM SP-2 show that the selection algorithms are effective in improving the performances of application programs that use sparse data sets.
机译:在最近的工作中,我们一直在为Fortran 90的数组内在函数提供并行稀疏支持。我们的支持库使用两层设计。在低级例程中,它要求使用数组函数的压缩/分配方案指定输入稀疏矩阵。在高级表示形式中,稀疏数组函数因Fortran 90数组固有接口而过载,因此程序员无需担心低级细节。通过自动选择和提供稀疏数组的分布和压缩方案,在将高级表示转换为低级例程的策略中,这引起了一个非常有趣的优化问题。我们提出了针对此优化问题的解决方案。优化问题显示为NP难的。我们开发了一种基于带注释的程序图的启发式算法,该算法被证明是实用的。 IBM SP-2上的实验结果表明,选择算法可有效提高使用稀疏数据集的应用程序的性能。

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