FUSED-PAGERANK：基于环融合的近似PageRank
FUSED-PAGERANK: Loop-Fusion based Approximate PageRank
摘要：
PageRank是一个图中心度度量，它给出了给定图中每个节点的重要性。PageRank算法提供了重要的见解，通过它们与其他节点形成的连接来理解节点的行为。它是一种迭代算法，在每次迭代中对节点进行排序，直到所有节点值收敛。PageRank算法使用稀疏存储格式实现，这导致代码中的内存访问不规则。这个关键特性抑制了优化以提高其性能，并使优化PageRank算法成为一个重要的问题。在这项工作中，我们提高了PageRank算法的性能，减少其不规则的内存访问。在本文中，我们提出了FUSED-PAGERANK算法，一个面向编译器优化的近似技术，减少了不规则的内存访问的PageRank算法的数量，提高其局部性，同时使算法的收敛速度更快，结果更准确。特别是，我们提出了一个近似的PageRank算法使用循环融合。我们相信，我们的工作是第一次正式应用传统的编译器优化技术的不规则内存访问的PageRank算法。我们已经通过在各种数据集上进行实验验证了我们的方法：LAW图，SNAP数据集和合成数据集。在这些基准测试中，我们已经实现了2.05X，2.23X，1.74X与顺序版本的FUSED-PAGERANK算法的最大加速比（vs. -O3优化），和~4.4X，~2.61X，~4.22X与并行版本的PageRank算法相比，边缘为中心的版本。
英文摘要：
PageRank is a graph centrality metric that gives the importance of each node in a given graph. The PageRank algorithm provides important insights to understand the behavior of nodes through the connections they form with other nodes. It is an iterative algorithm that ranks the nodes in each iteration until all the node values converge. The PageRank algorithm is implemented using sparse storage format, which results in irregular memory accesses in the code. This key feature inhibits optimizations to improve its performance, and makes optimizing the PageRank algorithm a non-trivial problem. In this work we improve the performance of PageRank algorithm by reducing its irregular memory accesses. In this paper, we propose FUSED-PAGERANK algorithm, a compiler optimization oriented approximate technique that reduces the number of irregular memory accesses in the PageRank algorithm, improving its locality while making the convergence of the algorithm faster with better accuracy in results. In particular, we propose an approximate PageRank algorithm using Loop-Fusion. We believe that ours is the first work that formally applies traditional compiler optimization techniques for irregular memory access in the PageRank algorithm. We have verified our method by performing experiments on a variety of datasets: LAW graphs, SNAP datasets and synthesized datasets. On these benchmarks, we have achieved a maximum speedup (vs. -O3 optimization) of 2.05X, 2.23X, 1.74X with sequential version, and ~4.4X, ~2.61X, ~4.22X with parallel version of FUSED-PAGERANK algorithm in comparison with Edge-centric version of PageRank algorithm.
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