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Exploring big graph computing — An empirical study from architectural perspective

机译:探索大图计算—从架构角度进行的实证研究

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Graph computing is widely applied in a large number of big data applications. Despite its importance, high performance graph computing remains a challenge, especially for large-scale graphs. In this paper, by analyzing from the architectural perspective, we study computational behaviors of graph computing in real-world use cases. We benchmark a set of representative graph algorithms implemented on a unified framework and conduct experiments to analyze comprehensive performance characteristics. In the characterization, we observed multiple insights, including irregular memory patterns, significant diverse behavior across different computations, highly data dependent behaviors, etc., using large-scale synthetic and real-world graphs. To the best of our knowledge, this is the first comprehensive architectural study on the full-scope of graph computing. It can improve our understanding on graph computing and help high performance computing research for graph-based big data applications.
机译:图计算被广泛应用于大量的大数据应用中。尽管它的重要性,但高性能图形计算仍然是一个挑战,特别是对于大型图形而言。在本文中,通过从体系结构的角度进行分析,我们研究了在实际用例中图计算的计算行为。我们对在统一框架上实现的一组代表性图形算法进行基准测试,并进行实验以分析综合性能特征。在表征过程中,我们使用大规模合成图和真实世界图观察了多种见解,包括不规则内存模式,跨不同计算的显着不同行为,高度依赖数据的行为等。据我们所知,这是第一个全面的图形计算综合架构研究。它可以增进我们对图计算的理解,并有助于基于图的大数据应用程序的高性能计算研究。

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