High-Performance GPU Implementation of PageRank with Reduced Precision Based on Mantissa Segmentation

Abstract

We address the acceleration of the PageRank al- gorithm for web information retrieval on graphics processing units (GPUs) via a modular precision framework that adapts the data format in memory to the numerical requirements as the iteration converges. In detail, we abandon the IEEE 754 single- and double-precision number representation formats, employed in the standard implementation of PageRank, to instead store the data in memory in some specialized formats. Furthermore, we avoid the data duplication by leveraging a data layout based on mantissa segmentation. Our evaluation on a V100 graphics card from NVIDIA shows acceleration factors of up to 30% with respect to the standard algorithm operating in double-precision.