一种改进优化的海量图实用无阻塞PageRank算法
An Improved and Optimized Practical Non-Blocking PageRank Algorithm for Massive Graphs
摘要：
PageRank是一个众所周知的算法，其鲁棒性有助于在处理图形和分析问题时设置标准基准。PageRank算法是许多图分析的标准，也是在推荐系统中提取图特征和预测用户评级的基础。PageRank算法不断迭代，更新页面的排名，直到达到收敛。然而，PageRank算法在大规模图上的实现是一个困难的任务，在共享内存架构上利用细粒度并行。并行PageRank内核在大型图形和共享内存架构上使用不同的编程模型的实验研究和分析已经得到了广泛的研究。本文提出了异步执行的PageRank算法，以利用计算上的海量图形，特别是在共享内存架构。我们评估了我们提出的非阻塞算法的性能，用于在真实世界和合成数据集上使用Posix多线程库在56核英特尔（R）至强处理器上进行PageRank计算。我们观察到，我们的异步实现相对于顺序运行实现了10倍到30倍的加速，相对于同步变体实现了5倍到10倍的改进。
英文摘要：
PageRank is a well-known algorithm whose robustness helps set a standard benchmark when processing graphs and analytical problems. The PageRank algorithm serves as a standard for many graph analytics and a foundation for extracting graph features and predicting user ratings in recommendation systems. The PageRank algorithm iterates continuously, updating the ranks of the pages till convergence is achieved. Nevertheless, the implementation of the PageRank algorithm on large-scale graphs that on shared memory architecture utilizing fine-grained parallelism is a difficult task at hand. The experimental study and analysis of the Parallel PageRank kernel on large graphs and shared memory architectures using different programming models have been studied extensively. This paper presents the asynchronous execution of the PageRank algorithm to leverage the computations on massive graphs, especially on shared memory architectures. We evaluate the performance of our proposed non-blocking algorithms for PageRank computation on real-world and synthetic datasets using Posix Multithreaded Library on a 56 core Intel(R) Xeon processor. We observed that our asynchronous implementations achieve 10x to 30x speedup with respect to sequential runs and 5x to 10x improvements over synchronous variants.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
