research-article

HYPHA: a framework based on separation of parallelisms to accelerate persistent homology matrix reduction

Authors:

Xiaodong ZhangAuthors Info & Claims

ICS '19: Proceedings of the ACM International Conference on Supercomputing

Pages 69 - 81

https://doi.org/10.1145/3330345.3332147

Published: 26 June 2019 Publication History

Abstract

Persistent homology (PH) matrix reduction is an important tool for data analytics in many application areas. Due to its highly irregular execution patterns in computation, it is challenging to gain high efficiency in parallel processing for increasingly large data sets.

In this paper, we introduce HYPHA, a HYbrid Persistent Homology matrix reduction Accelerator, to make parallel processing highly efficient on both GPU and multicore. The essential foundation of our algorithm design and implementation is the separation of SIMT and MIMD parallelisms in PH matrix reduction computation. With such a separation, we are able to perform massive parallel scanning operations on GPU in a super-fast manner, which also collects rich information from an input boundary matrix for further parallel reduction operations on multicore with high efficiency. The HYPHA framework may provide a general purpose guidance to high performance computing on multiple hardware accelerators.

To our best knowledge, HYPHA achieves the highest performance in PH matrix reduction execution. Our experiments show speedups of up to 116x against the standard PH algorithm. Compared to the state-of-the-art parallel PH software packages, such as PHAT and DIPHA, HYPHA outperforms their fastest PH matrix reduction algorithms by factor up to ~2.3x.

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Cited By

Luo YNelson B(2024)Accelerating Iterated Persistent Homology Computations with Warm StartsComputational Geometry10.1016/j.comgeo.2024.102089(102089)Online publication date: Mar-2024
https://doi.org/10.1016/j.comgeo.2024.102089
Malott NChen SWilsey P(2022)A Survey on the High Performance Computation of Persistent HomologyIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.3147070(1-1)Online publication date: 2022
https://doi.org/10.1109/TKDE.2022.3147070
Qin AXiao MWu YHuang XZhang X(2021)MixerProceedings of the VLDB Endowment10.14778/3476311.347637114:12(2906-2917)Online publication date: 28-Oct-2021
https://dl.acm.org/doi/10.14778/3476311.3476371
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cover image ACM Conferences

ICS '19: Proceedings of the ACM International Conference on Supercomputing

June 2019

533 pages

ISBN:9781450360791

DOI:10.1145/3330345

General Chair:
Rudolf Eigenmann
University of Delaware
,
Program Chairs:
Chen Ding
University of Rochester
,
Sally A. McKee
Clemson University

Copyright © 2019 ACM.

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Published: 26 June 2019

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ICS '19: 2019 International Conference on Supercomputing

June 26 - 28, 2019

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Cited By

Luo YNelson B(2024)Accelerating Iterated Persistent Homology Computations with Warm StartsComputational Geometry10.1016/j.comgeo.2024.102089(102089)Online publication date: Mar-2024
https://doi.org/10.1016/j.comgeo.2024.102089
Malott NChen SWilsey P(2022)A Survey on the High Performance Computation of Persistent HomologyIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.3147070(1-1)Online publication date: 2022
https://doi.org/10.1109/TKDE.2022.3147070
Qin AXiao MWu YHuang XZhang X(2021)MixerProceedings of the VLDB Endowment10.14778/3476311.347637114:12(2906-2917)Online publication date: 28-Oct-2021
https://dl.acm.org/doi/10.14778/3476311.3476371
Malott NVerma RSingh RWilsey P(2021)Distributed Computation of Persistent Homology from Partitioned Big Data2021 IEEE International Conference on Cluster Computing (CLUSTER)10.1109/Cluster48925.2021.00050(344-354)Online publication date: Sep-2021
https://doi.org/10.1109/Cluster48925.2021.00050
Bauer U(2021)Ripser: efficient computation of Vietoris–Rips persistence barcodesJournal of Applied and Computational Topology10.1007/s41468-021-00071-55:3(391-423)Online publication date: 17-Jun-2021
https://doi.org/10.1007/s41468-021-00071-5
Morozov DNigmetov AScheideler CSpear M(2020)Towards Lockfree Persistent HomologyProceedings of the 32nd ACM Symposium on Parallelism in Algorithms and Architectures10.1145/3350755.3400244(555-557)Online publication date: 6-Jul-2020
https://dl.acm.org/doi/10.1145/3350755.3400244
Carlsson G(2020)Topological methods for data modellingNature Reviews Physics10.1038/s42254-020-00249-32:12(697-708)Online publication date: 10-Nov-2020
https://doi.org/10.1038/s42254-020-00249-3
Zhang X(2019)Software system research in post-Moore’s Law era: a historical perspective for the futureScience China Information Sciences10.1007/s11432-019-9860-162:9Online publication date: 5-Aug-2019
https://doi.org/10.1007/s11432-019-9860-1

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