Parallel GraphBLAS with OpenMP
Parallel GraphBLAS with OpenMP
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与 OpenMP 并行 GraphBLAS
DOI：
10.1137/1.9781611976229.14
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发表时间：
2020
期刊：
影响因子：
0.6
通讯作者：
Gábor Szárnyas
中科院分区：
文献类型：
--
作者：
M. Aznaveh;Jinhao Chen;T. Davis;Bálint Hegyi;Scott P. Kolodziej;T. Mattson;Gábor Szárnyas
SuiteSparse:GraphBLAS 是 GraphBLAS 标准的完整实现。它提供了一个强大且富有表现力的框架，用于创建基于半环上稀疏矩阵运算的优雅数学的图算法。使用 GraphBLAS 编写的算法可以用最短的开发时间实现高性能。通过 OpenMP 实现的多线程并行性提供了额外的加速，我们在 20 核 Intel® Xeon® E5-2698 CPU 系统上解决各种问题（三角形计数、k-truss、广度优先搜索、Bellman-Ford、局部聚类系数和稀疏深度神经网络问题）时进行了说明。各种各样的算法说明了 GraphBLAS API 创建新图算法的表现力。我们使用新开发的 Suite-Sparse:GraphBLAS v3.0.1 在一组大型现实世界图上展示这些算法的性能结果。
SuiteSparse:GraphBLAS is a complete implementation of the GraphBLAS standard. It provides a powerful and expressive framework for creating graph algorithms based on the elegant mathematics of sparse matrix operations on a semiring. Algorithms written with the GraphBLAS achieve high performance with minimal development time. Multithreaded parallelism through OpenMP provides additional speedup, which we illustrate on a 20-core Intel ® Xeon ® E5-2698 CPU system when solving various problems (triangle counting, k -truss, breadth-first search, Bellman-Ford, local clustering coefficient, and a sparse deep neural network problem). This wide variety of algorithms illustrates the expressiveness of the GraphBLAS API to create new graph algorithms. We present performance results with these algorithms on a set of large real-world graphs, using the newly developed Suite-Sparse:GraphBLAS v3.0.1.