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Task-parallel Analysis of Molecular Dynamics Trajectories

Authors:

Ioannis Paraskevakos,

Mahzad Khoshlessan,

George Chantzialexiou,

Thomas E. Cheatham,

Oliver Beckstein,

Geoffrey C. Fox,

Shantenu JhaAuthors Info & Claims

ICPP '18: Proceedings of the 47th International Conference on Parallel Processing

Article No.: 49, Pages 1 - 10

https://doi.org/10.1145/3225058.3225128

Published: 13 August 2018 Publication History

Abstract

Different parallel frameworks for implementing data analysis applications have been proposed by the HPC and Big Data communities. In this paper, we investigate three task-parallel frameworks: Spark, Dask and RADICAL-Pilot with respect to their ability to support data analytics on HPC resources and compare them to MPI. We investigate the data analysis requirements of Molecular Dynamics (MD) simulations which are significant consumers of supercomputing cycles, producing immense amounts of data. A typical large-scale MD simulation of a physical system of O(100k) atoms over μsecs can produce from O(10) GB to O(1000) GBs of data. We propose and evaluate different approaches for parallelization of a representative set of MD trajectory analysis algorithms, in particular the computation of path similarity and leaflet identification. We evaluate Spark, Dask and RADICAL-Pilot with respect to their abstractions and runtime engine capabilities to support these algorithms. We provide a conceptual basis for comparing and understanding different frameworks that enable users to select the optimal system for each application. We also provide a quantitative performance analysis of the different algorithms across the three frameworks.

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Cited By

Mandal DShukla KGhosh A(2023)Molecular Dynamics Simulation by Implementing Parallel Computing and Big Data Principles2023 IEEE IAS Global Conference on Emerging Technologies (GlobConET)10.1109/GlobConET56651.2023.10150063(1-6)Online publication date: 19-May-2023
https://doi.org/10.1109/GlobConET56651.2023.10150063
Dugré MHayot‐Sasson VGlatard T(2023)Performance comparison of Dask and Apache Spark on HPC systems for neuroimagingConcurrency and Computation: Practice and Experience10.1002/cpe.763535:21Online publication date: 22-Jan-2023
https://doi.org/10.1002/cpe.7635
Moreno MVilaça RFerreira P(2022)Scalable transcriptomics analysis with Dask: applications in data science and machine learningBMC Bioinformatics10.1186/s12859-022-05065-323:1Online publication date: 30-Nov-2022
https://doi.org/10.1186/s12859-022-05065-3
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ICPP '18: Proceedings of the 47th International Conference on Parallel Processing

August 2018

945 pages

ISBN:9781450365109

DOI:10.1145/3225058

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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University of Oregon: University of Oregon

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Published: 13 August 2018

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ICPP 2018

ICPP 2018: 47th International Conference on Parallel Processing

August 13 - 16, 2018

OR, Eugene, USA

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ICPP '18 Paper Acceptance Rate 91 of 313 submissions, 29%;

Overall Acceptance Rate 91 of 313 submissions, 29%

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Cited By

Mandal DShukla KGhosh A(2023)Molecular Dynamics Simulation by Implementing Parallel Computing and Big Data Principles2023 IEEE IAS Global Conference on Emerging Technologies (GlobConET)10.1109/GlobConET56651.2023.10150063(1-6)Online publication date: 19-May-2023
https://doi.org/10.1109/GlobConET56651.2023.10150063
Dugré MHayot‐Sasson VGlatard T(2023)Performance comparison of Dask and Apache Spark on HPC systems for neuroimagingConcurrency and Computation: Practice and Experience10.1002/cpe.763535:21Online publication date: 22-Jan-2023
https://doi.org/10.1002/cpe.7635
Moreno MVilaça RFerreira P(2022)Scalable transcriptomics analysis with Dask: applications in data science and machine learningBMC Bioinformatics10.1186/s12859-022-05065-323:1Online publication date: 30-Nov-2022
https://doi.org/10.1186/s12859-022-05065-3
Friedemann SRaffin B(2022)An elastic framework for ensemble-based large-scale data assimilationThe International Journal of High Performance Computing Applications10.1177/1094342022111050736:4(543-563)Online publication date: 28-Jun-2022
https://doi.org/10.1177/10943420221110507
Luckow AJha S(2020)Methods and Experiences for Developing Abstractions for Data-intensive, Scientific Applications2020 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)10.1109/IPDPSW50202.2020.00106(636-645)Online publication date: May-2020
https://doi.org/10.1109/IPDPSW50202.2020.00106
Khoshlessan MParaskevakos IFox GJha SBeckstein O(2020)Parallel performance of molecular dynamics trajectory analysisConcurrency and Computation: Practice and Experience10.1002/cpe.578932:19Online publication date: 27-Apr-2020
https://doi.org/10.1002/cpe.5789
Hayot-Sasson VBrown SGlatard T(2019)Performance Evaluation of Big Data Processing Strategies for Neuroimaging2019 19th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID)10.1109/CCGRID.2019.00059(449-458)Online publication date: May-2019
https://doi.org/10.1109/CCGRID.2019.00059

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