<span class="var-sub_title">High-Performance GPU Implementation of PageRank with Reduced Precision Based on Mantissa Segmentation</span> SC18 Proceedings

The International Conference for High Performance Computing, Networking, Storage, and Analysis

IA^3 2018: 8th Workshop on Irregular Applications: Architectures and Algorithms


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

Authors: Hartwig Anzt (Karlsruhe Institute of Technology)

Abstract: We address the acceleration of the PageRank algorithm for web information retrieval on graphics processing units (GPUs) via a modular precision framework that adapts the input 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.

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