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首页> 外文期刊>IEEE Transactions on Parallel and Distributed Systems >Exploiting locality for irregular scientific codes
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Exploiting locality for irregular scientific codes

机译：利用不规范的科学法规的地方

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摘要

Irregular scientific codes experience poor cache performance due to their irregular memory access patterns. In this paper, we present two new locality improving techniques for irregular scientific codes. Our techniques exploit geometric structures hidden in data access patterns and computation structures. Our new data reordering (GPART) finds the graph structure within data accesses and applies hierarchical clustering. Quality partitions are constructed quickly by clustering multiple neighbor nodes with priority on nodes with high degree and repeating a few passes. Overhead is kept low by clustering multiple nodes in each pass and considering only edges between partitions. Our new computation reordering (Z-SORT) treats the values of index arrays as coordinates and reorders corresponding computations in Z-curve order. Applied to dense inputs, Z-SORT achieves performance close to data reordering combined with other computation reordering but without the overhead involved in data reordering. Experiments on irregular scientific codes for a variety of meshes show locality optimization techniques are effective for both sequential and parallelized codes, improving performance by 60-87 percent. GPART achieved within 1-2 percent of the performance of more sophisticated partitioning algorithms, but with one third of the overhead. Z-SORT also yields the performance improvement of 64 percent for dense inputs, which is comparable with data reordering combined with computation reordering.
机译：不规则的科学代码由于其不规则的内存访问模式而导致缓存性能不佳。在本文中，我们针对不规则的科学规范提出了两种新的地区改进技术。我们的技术利用隐藏在数据访问模式和计算结构中的几何结构。我们的新数据重新排序（GPART）在数据访问中找到图结构，并应用分层聚类。通过对多个相邻节点进行聚类（优先级高的节点）并重复几次遍历，可以快速构建质量分区。通过在每次通过中对多个节点进行群集并仅考虑分区之间的边缘，可以将开销保持在较低水平。我们的新计算重新排序（Z-SORT）将索引数组的值视为坐标，并以Z曲线顺序对相应的计算重新排序。应用于密集输入时，Z-SORT的性能接近于将数据重新排序与其他计算重新排序相结合的功能，而无需进行数据重新排序所涉及的开销。针对各种网格的不规则科学代码的实验表明，局部性优化技术对于顺序代码和并行代码均有效，可将性能提高60-87％。 GPART达到了更复杂的分区算法性能的1-2％，但开销却只有三分之一。对于密集输入，Z-SORT还可以将性能提高64％，这可与数据重新排序和计算重新排序相媲美。

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