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Mixed-precision computation has been proposed as a means to accelerate iterative algorithms as it can reduce the memory bandwidth and cache effectiveness. This paper aims for further memory traffic reduction via introducing new half-precision (16 bit) data formats customized for PageRank. We develop two formats. A first format builds on the observation that the exponents of about 99% of PageRank values are tightly distributed around the exponent of the inverse of the number of vertices. A second format builds on the observation that 6 exponent bits are sufficient to capture the full dynamic range of PageRank values. Our floating-point formats provide less precision compared to standard IEEE 754 formats, but sufficient dynamic range for PageRank. The experimental results on various size graphs show that the proposed formats can achieve an accuracy of le-4., which is an improvement over the state of the art. Due to random memory access patterns in the algorithm, performance improvements over our highly tuned baseline are 1.5% at best.
|Title of host publication||2020 IEEE High Performance Extreme Computing Conference, HPEC 2020|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Publication status||Published - 22 Dec 2020|
|Event||2020 IEEE High Performance Extreme Computing Conference, HPEC 2020 - Virtual, Waltham, United States|
Duration: 21 Sep 2020 → 25 Sep 2020
|Name||IEEE High Performance Extreme Computing Conference: Proceedings|
|Conference||2020 IEEE High Performance Extreme Computing Conference, HPEC 2020|
|Period||21/09/2020 → 25/09/2020|
Bibliographical notePublisher Copyright:
© 2020 IEEE.
Copyright 2021 Elsevier B.V., All rights reserved.
- Customized Floating-Point Data Format
- Graph Processing
- Transprecision Computing
ASJC Scopus subject areas
- Artificial Intelligence
- Computational Theory and Mathematics
- Computer Networks and Communications
- Computer Science Applications
- Hardware and Architecture
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