Scalable parallel algorithm for fast computation of Transitive Closure of Graphs on Shared Memory Architectures

Sarthak Patel, Bhrugu Dave, Smit Kumbhani, Mihir Desai, Sidharth Kumar, Bhaskar Chaudhury
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引用次数: 1

Abstract

We present a scalable algorithm that computes the transitive closure of a graph on shared memory architectures using the OpenMP API in C++. Two different parallelization strategies have been presented and the performance of the two algorithms has been compared for several data-sets of varying sizes. We demonstrate the scalability of the best parallel implementation up to 176 threads on a shared memory architecture, by producing a graph with more than 3.82 trillion edges. To the best of our knowledge, this is the first implementation that has computed the transitive closure of such a large graph on a shared memory system. Optimization strategies for better cache utilization for large data-sets have been discussed. The important issue of load balancing has been analyzed and its mitigation using the optimal OpenMP scheduling clause has been discussed in detail.
共享内存架构下图传递闭包快速计算的可扩展并行算法
我们提出了一种可扩展的算法,该算法使用c++中的OpenMP API计算共享内存架构上图的传递闭包。提出了两种不同的并行化策略,并对两种算法在不同规模的数据集上的性能进行了比较。我们通过生成具有超过3.82万亿个边的图,展示了共享内存架构上最多176个线程的最佳并行实现的可扩展性。据我们所知,这是第一个在共享内存系统上计算如此大的图的传递闭包的实现。本文还讨论了提高大型数据集缓存利用率的优化策略。分析了负载平衡的重要问题,并详细讨论了使用最优OpenMP调度条款来缓解负载平衡的问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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