research-article

Acceleration of PageRank with Customized Precision Based on Mantissa Segmentation

Authors:

Thomas Grützmacher,

Enrique S. Quintana-OrtíAuthors Info & Claims

ACM Transactions on Parallel Computing (TOPC), Volume 7, Issue 1

Article No.: 4, Pages 1 - 19

https://doi.org/10.1145/3380934

Published: 09 March 2020 Publication History

Abstract

We describe the application of a communication-reduction technique for the PageRank algorithm that dynamically adapts the precision of the data access to the numerical requirements of the algorithm as the iteration converges. Our variable-precision strategy, using a customized precision format based on mantissa segmentation (CPMS), abandons the IEEE 754 single- and double-precision number representation formats employed in the standard implementation of PageRank, and instead handles the data in memory using a customized floating-point format. The customized format enables fast data access in different accuracy, prevents overflow/underflow by preserving the IEEE 754 double-precision exponent, and efficiently avoids data duplication, since all bits of the original IEEE 754 double-precision mantissa are preserved in memory, but re-organized for efficient reduced precision access. With this approach, the truncated values (omitting significand bits), as well as the original IEEE double-precision values, can be retrieved without duplicating the data in different formats.

Our numerical experiments on an NVIDIA V100 GPU (Volta architecture) and a server equipped with two Intel Xeon Platinum 8168 CPUs (48 cores in total) expose that, compared with a standard IEEE double-precision implementation, the CPMS-based PageRank completes about 10% faster if high-accuracy output is needed, and about 30% faster if reduced output accuracy is acceptable.

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Cited By

Mukunoki DKawai MImamura T(2023)Sparse Matrix-Vector Multiplication with Reduced-Precision Memory Accessor2023 IEEE 16th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC)10.1109/MCSoC60832.2023.00094(608-615)Online publication date: 18-Dec-2023
https://doi.org/10.1109/MCSoC60832.2023.00094
Vandierendonck HRauchwerger LCameron KNikolopoulos DPnevmatikatos D(2022)Software-defined floating-point number formats and their application to graph processingProceedings of the 36th ACM International Conference on Supercomputing10.1145/3524059.3532360(1-17)Online publication date: 28-Jun-2022
https://dl.acm.org/doi/10.1145/3524059.3532360
Makkar A(2022)SecureEngine: Spammer classification in cyber defence for leveraging green computing in Sustainable citySustainable Cities and Society10.1016/j.scs.2021.10365879(103658)Online publication date: Apr-2022
https://doi.org/10.1016/j.scs.2021.103658
Show More Cited By

Index Terms

Acceleration of PageRank with Customized Precision Based on Mantissa Segmentation
1. Computing methodologies
  1. Parallel computing methodologies
    1. Parallel algorithms
      1. Massively parallel algorithms
2. Mathematics of computing
  1. Mathematical analysis
    1. Numerical analysis
      1. Computations on matrices

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cover image ACM Transactions on Parallel Computing

ACM Transactions on Parallel Computing Volume 7, Issue 1

Special Issue on Innovations in Systems for Irregular Applications, Part 1 and Regular Paper

March 2020

182 pages

ISSN:2329-4949

EISSN:2329-4957

DOI:10.1145/3387354

Editor:
David A. Bader
New Jersey Institute of Technology, USA

Issue’s Table of Contents

Copyright © 2020 ACM.

© 2020 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 March 2020

Accepted: 01 November 2019

Revised: 01 July 2019

Received: 01 November 2018

Published in TOPC Volume 7, Issue 1

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Cited By

Mukunoki DKawai MImamura T(2023)Sparse Matrix-Vector Multiplication with Reduced-Precision Memory Accessor2023 IEEE 16th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC)10.1109/MCSoC60832.2023.00094(608-615)Online publication date: 18-Dec-2023
https://doi.org/10.1109/MCSoC60832.2023.00094
Vandierendonck HRauchwerger LCameron KNikolopoulos DPnevmatikatos D(2022)Software-defined floating-point number formats and their application to graph processingProceedings of the 36th ACM International Conference on Supercomputing10.1145/3524059.3532360(1-17)Online publication date: 28-Jun-2022
https://dl.acm.org/doi/10.1145/3524059.3532360
Makkar A(2022)SecureEngine: Spammer classification in cyber defence for leveraging green computing in Sustainable citySustainable Cities and Society10.1016/j.scs.2021.10365879(103658)Online publication date: Apr-2022
https://doi.org/10.1016/j.scs.2021.103658
Jahanshahi SMolahosseini AZarandi A(2022)uLog: a software-based approximate logarithmic number system for computations on SIMD processorsThe Journal of Supercomputing10.1007/s11227-022-04713-y79:2(1750-1783)Online publication date: 2-Aug-2022
https://dl.acm.org/doi/10.1007/s11227-022-04713-y
Grützmacher TAnzt HQuintana‐Ortí E(2021)Using Ginkgo's memory accessor for improving the accuracy of memory‐bound low precision BLASSoftware: Practice and Experience10.1002/spe.304153:1(81-98)Online publication date: 17-Oct-2021
https://doi.org/10.1002/spe.3041
Gu RBecchi M(2020)GPU-FPtuner: Mixed-precision Auto-tuning for Floating-point Applications on GPU2020 IEEE 27th International Conference on High Performance Computing, Data, and Analytics (HiPC)10.1109/HiPC50609.2020.00043(294-304)Online publication date: Dec-2020
https://doi.org/10.1109/HiPC50609.2020.00043
Molahosseini AVandierendonck H(2020)Half-Precision Floating-Point Formats for PageRank: Opportunities and Challenges2020 IEEE High Performance Extreme Computing Conference (HPEC)10.1109/HPEC43674.2020.9286179(1-7)Online publication date: 22-Sep-2020
https://doi.org/10.1109/HPEC43674.2020.9286179
Sabbagh Molahosseini ASousa LEmrani Zarandi AVandierendonck H(2012)Low-Precision Floating-Point Formats: From General-Purpose to Application-SpecificApproximate Computing10.1007/978-3-030-98347-5_4(77-98)Online publication date: 24-Feb-2012
https://doi.org/10.1007/978-3-030-98347-5_4

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