FPGA的子流中心最大匹配

Maciej Besta, Marc Fischer, Tal Ben-Nun, D. Stanojevic, J. D. F. Licht, T. Hoefler
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引用次数: 0

摘要

在社会网络分析、计算科学、调度和其他领域,开发高性能和节能的最大匹配算法变得越来越重要。在这项工作中,我们提出了第一个为fpga设计的最大匹配算法;它是节能的,并且在准确性、性能和存储利用率方面有可证明的保证。为了实现这一点,我们放弃了流行的图形处理范例,例如以顶点为中心的编程,这通常需要大量的通信成本。相反,我们提出了一种以子流为中心的方法,在这种方法中,数据的输入流被分成独立处理的子流,以实现更多的并行性,同时降低通信成本。我们以流图算法理论为基础,分析了14种模型和28种算法。我们使用此分析来提供与FPGA平台的物理约束相匹配的理论基础。我们的算法提供高性能(比调优的并行CPU变体加速4倍以上)、低内存、高精度和有效使用FPGA资源。以子流为中心的方法可以很容易地扩展到其他算法中,从而在fpga上提供低功耗和高性能的图形处理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Substream-Centric Maximum Matchings on FPGA
Developing high-performance and energy-efficient algorithms for maximum matchings is becoming increasingly important in social network analysis, computational sciences, scheduling, and others. In this work, we propose the first maximum matching algorithm designed for FPGAs; it is energy-efficient and has provable guarantees on accuracy, performance, and storage utilization. To achieve this, we forego popular graph processing paradigms, such as vertex-centric programming, that often entail large communication costs. Instead, we propose a substream-centric approach, in which the input stream of data is divided into substreams processed independently to enable more parallelism while lowering communication costs. We base our work on the theory of streaming graph algorithms and analyze 14 models and 28 algorithms. We use this analysis to provide theoretical underpinning that matches the physical constraints of FPGA platforms. Our algorithm delivers high performance (more than 4× speedup over tuned parallel CPU variants), low memory, high accuracy, and effective usage of FPGA resources. The substream-centric approach could easily be extended to other algorithms to offer low-power and high-performance graph processing on FPGAs.
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