  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.
