PageRank kernel is a standard benchmark addressing various graph processing 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 is an iterative algorithm that continuously updates the ranks of pages until it converges to a value. However, implementing the PageRank algorithm on a shared memory architecture while taking advantage of fine-grained parallelism with large-scale graphs is hard to implement. The experimental study and analysis of the parallel PageRank metric 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 \(10\times\) to \(30\times\) speed-up with respect to sequential runs and \(5\times\) to \(10\times\) improvements over synchronous variants.

PageRank 内核是解决各种图形处理和分析问题的标准基准。PageRank 算法是许多图形分析的标准，也是在推荐系统中提取图形特征和预测用户评分的基础。PageRank算法是一种迭代算法，它不断更新页面的排名，直到收敛到一个值。然而，在共享内存架构上实现 PageRank 算法，同时利用大规模图的细粒度并行性是很难实现的。对使用不同编程模型的大型图和共享内存架构的并行 PageRank 度量的实验研究和分析已得到广泛研究。本文介绍了 PageRank 算法的异步执行，以利用海量图上的计算，尤其是在共享内存架构上。我们使用 56 核 Intel(R) Xeon 处理器上的 POSIX 多线程库评估我们提出的用于 PageRank 计算的非阻塞算法在现实世界和合成数据集上的性能。我们观察到我们的异步实现实现了\(10\times\)到\(30\times\)相对于顺序运行的加速和\(5\times\)到\(10\times\)的改进。