Due to massive thread-level parallelism, GPUs have become an attractive platform for accelerating large-scale data parallel computations, such as graph processing. However, achieving high performance for graph processing with GPUs is non-trivial. Processing graphs on GPUs introduces several problems, such as load imbalance, low utilization of hardware unit, and memory divergence. Although previous work has proposed several software strategies to optimize graph processing on GPUs, there are several issues beyond the capability of software techniques to address. In this article, we present GraphPEG, a graph processing engine for efficient graph processing on GPUs. Inspired by the observation that many graph algorithms have a common pattern on graph traversal, GraphPEG improves the performance of graph processing by coupling automatic edge gathering with fine-grain work distribution. GraphPEG can also adapt to various input graph datasets and simplify the software design of graph processing with hardware-assisted graph traversal. Simulation results show that, in comparison with two representative highly efficient GPU graph processing software framework Gunrock and SEP-Graph, GraphPEG improves graph processing throughput by 2.8× and 2.5× on average, and up to 7.3× and 7.0× for six graph algorithm benchmarks on six graph datasets, with marginal hardware cost.

中文翻译：

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图PEG

由于大规模线程级并行性，GPU 已成为加速大规模数据并行计算（如图形处理）的有吸引力的平台。然而，使用 GPU 实现图形处理的高性能并非易事。在 GPU 上处理图引入了几个问题，例如负载不平衡、硬件单元利用率低和内存发散。尽管之前的工作已经提出了几种软件策略来优化 GPU 上的图形处理，但还有一些问题超出了软件技术的解决能力。在本文中，我们介绍了 GraphPEG，这是一种用于在 GPU 上进行高效图形处理的图形处理引擎。受到许多图算法在图遍历上具有共同模式的观察的启发，GraphPEG 通过将自动边缘收集与细粒度工作分布相结合，提高了图形处理的性能。GraphPEG还可以适应各种输入图数据集，并通过硬件辅助图遍历简化图处理的软件设计。仿真结果表明，与两个具有代表性的高效 GPU 图处理软件框架 Gunrock 和 SEP-Graph 相比，GraphPEG 的图处理吞吐量平均提高了 2.8 倍和 2.5 倍，在六个图算法基准测试中分别提高了 7.3 倍和 7.0 倍。在六个图形数据集上，具有边际硬件成本。