基于幂律图变换的快速高效并行广度优先搜索
Fast and Efficient Parallel Breadth-First Search with Power-law Graph Transformation
AI总结：
本文提出基于RCM重排序和SIMD加速的BFS预处理方法，在ARMv8上达326.48 MTEPS/W，位列Green Graph500第二。
摘要：
在大数据时代，图计算被广泛应用于从现实世界图中挖掘隐藏价值，涉及社交网络、知识图谱、网页搜索和推荐系统等多种场景。然而，随机内存访问导致缓存利用效率低下，且不规则的度分布造成显著的负载不均衡。广度优先搜索（BFS）常被用作许多重要且复杂的图算法的内核。本文描述了一种使用反向Cuthill-Mckee（RCM）算法的预处理方法来提高数据局部性，并展示了如何为BFS实现高效的负载均衡。对RCM重排序后的图数据的计算也通过SIMD执行加速。我们在Graph500基准的Kronecker图和现实世界图上评估了图预处理方法的性能。我们在RCM重排序后的图数据上实现的BFS在ARMv8系统上达到了326.48 MTEPS/W（每瓦兆次遍历边数），在2020年6月的Green Graph500榜单中排名第二（第一名使用GPU加速）。
英文摘要：
In the big data era, graph computing is widely used to exploit the hidden value in real-world graphs in various scenarios such as social networks, knowledge graphs, web searching, and recommendation systems. However, the random memory accesses result in inefficient use of cache and the irregular degree distribution leads to substantial load imbalance. Breadth-First Search (BFS) is frequently utilized as a kernel for many important and complex graph algorithms. In this paper, we describe a preprocessing approach using Reverse Cuthill-Mckee (RCM) algorithm to improve data locality and demonstrate how to achieve an efficient load balancing for BFS. Computations on RCM-reordered graph data are also accelerated with SIMD executions. We evaluate the performance of the graph preprocessing approach on Kronecker graphs of the Graph500 benchmark and real-world graphs. Our BFS implementation on RCM-reordered graph data achieves 326.48 MTEPS/W (mega TEPS per watt) on an ARMv8 system, ranking 2nd on the Green Graph500 list in June 2020 (the 1st rank uses GPU acceleration).
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