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PageRank is a graph centrality metric that gives the importance of each nodein a given graph. The PageRank algorithm provides important insights tounderstand the behavior of nodes through the connections they form with othernodes. It is an iterative algorithm that ranks the nodes in each iterationuntil all the node values converge. The PageRank algorithm is implemented usingsparse storage format, which results in irregular memory accesses in the code.This key feature inhibits optimizations to improve its performance, and makesoptimizing the PageRank algorithm a non-trivial problem. In this work weimprove the performance of PageRank algorithm by reducing its irregular memoryaccesses. In this paper, we propose FUSED-PAGERANK algorithm, a compileroptimization oriented approximate technique that reduces the number ofirregular memory accesses in the PageRank algorithm, improving its localitywhile making the convergence of the algorithm faster with better accuracy inresults. In particular, we propose an approximate PageRank algorithm usingLoop-Fusion. We believe that ours is the first work that formally appliestraditional compiler optimization techniques for irregular memory access in thePageRank algorithm. We have verified our method by performing experiments on avariety of datasets: LAW graphs, SNAP datasets and synthesized datasets. Onthese benchmarks, we have achieved a maximum speedup (vs. -O3 optimization) of2.05X, 2.23X, 1.74X with sequential version, and ~4.4X, ~2.61X, ~4.22X withparallel version of FUSED-PAGERANK algorithm in comparison with Edge-centricversion of PageRank algorithm.

FUSED-PAGERANK: Loop-Fusion based Approximate PageRank