PGAbB：一个基于块的异构平台图形处理框架
PGAbB: A Block-Based Graph Processing Framework for Heterogeneous Platforms
摘要：
设计灵活的图形内核，可以在各种平台上运行良好，是一个至关重要的研究问题，由于频繁使用的图形建模数据和最近的架构的进步和品种。在这项工作中，我们提出了一种新的图形处理框架，PGAbB（块并行图算法），现代共享内存异构平台。我们的框架实现了一个基于块的编程模型。这允许用户使用对子图进行操作的内核来表达图算法。PGAbB支持适合主机DRAM但不适合GPU设备存储器的图形计算，并提供简单但有效的调度技术，以将计算调度到异构架构中的所有可用资源。我们已经证明，可以很容易地实现一组不同的图算法在我们的框架中开发五个算法。我们的实验结果表明，PGAbB实现实现更好的或有竞争力的性能相比，手工优化的实现。基于我们对五种图形算法和44种图形的实验，在中值中，PGAbB分别比GAPBS、Galois、Ligra、LAGraph Galois-GPU和Gunrock图形处理系统的性能高1.6、1.6、5.7、3.4、4.5和2.4倍。
英文摘要：
Designing flexible graph kernels that can run well on various platforms is a crucial research problem due to the frequent usage of graphs for modeling data and recent architectural advances and variety. In this work, we propose a novel graph processing framework, PGAbB (Parallel Graph Algorithms by Blocks), for modern shared-memory heterogeneous platforms. Our framework implements a block-based programming model. This allows a user to express a graph algorithm using kernels that operate on subgraphs. PGAbB support graph computations that fit in host DRAM but not in GPU device memory, and provides simple but effective scheduling techniques to schedule computations to all available resources in a heterogeneous architecture. We have demonstrated that one can easily implement a diverse set of graph algorithms in our framework by developing five algorithms. Our experimental results show that PGAbB implementations achieve better or competitive performance compared to hand-optimized implementations. Based on our experiments on five graph algorithms and forty-four graphs, in the median, PGAbB achieves 1.6, 1.6, 5.7, 3.4, 4.5, and 2.4 times better performance than GAPBS, Galois, Ligra, LAGraph Galois-GPU, and Gunrock graph processing systems, respectively.
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