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PageRank is a well-known algorithm whose robustness helps set a standardbenchmark when processing graphs and analytical problems. The PageRankalgorithm serves as a standard for many graph analytics and a foundation forextracting graph features and predicting user ratings in recommendationsystems. The PageRank algorithm iterates continuously, updating the ranks ofthe pages till convergence is achieved. Nevertheless, the implementation of thePageRank algorithm on large-scale graphs that on shared memory architectureutilizing fine-grained parallelism is a difficult task at hand. Theexperimental study and analysis of the Parallel PageRank kernel on large graphsand shared memory architectures using different programming models have beenstudied extensively. This paper presents the asynchronous execution of thePageRank algorithm to leverage the computations on massive graphs, especiallyon shared memory architectures. We evaluate the performance of our proposednon-blocking algorithms for PageRank computation on real-world and syntheticdatasets using Posix Multithreaded Library on a 56 core Intel(R) Xeonprocessor. We observed that our asynchronous implementations achieve 10x to 30xspeedup with respect to sequential runs and 5x to 10x improvements oversynchronous variants.

An Improved and Optimized Practical Non-Blocking PageRank Algorithm for Massive Graphs