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Title: Dynamic load balancing for a mesh‐based scientific application

Abstract

Summary CMT‐nek is a new scientific application for performing high fidelity predictive simulations of particle‐laden, explosively dispersed turbulent flows. CMT‐nek is compute‐intensive and targeted for deployment on exascale platforms. The moving particles are the primary source of load imbalance when the application is executed on parallel processors. In a demonstration problem, all the particles are initially in a closed container until a detonation occurs and the particles move apart. If all processors get an equal share of the fluid domain, then only some of the processors get sections of the domain that are initially laden with particles, leading to disparate loads on the processors. To eliminate load imbalance in different processors and to speed up the makespan, we present different load‐balancing algorithms for CMT‐nek on large‐scale multicore platforms. The load on a processor is determined using different techniques. The performance of the different load‐balancing algorithms is compared, and the associated overheads are analyzed. Evaluations of the application with and without load‐balancing are conducted, and these show that with load‐balancing, simulation time becomes faster by a factor of up to 9.97. The performance was further improved by a factor of up to 1.42 using machine‐learning–based algorithms.

Authors:
ORCiD logo [1];  [1];  [2];  [2];  [2];  [1]
  1. Department of Computer &, Information Science &, Engineering University of Florida Gainesville Florida
  2. Department of Mechanical &, Aerospace Engineering University of Florida Gainesville Florida
Publication Date:
Sponsoring Org.:
USDOE
OSTI Identifier:
1581451
Resource Type:
Publisher's Accepted Manuscript
Journal Name:
Concurrency and Computation. Practice and Experience
Additional Journal Information:
Journal Name: Concurrency and Computation. Practice and Experience Journal Volume: 32 Journal Issue: 9; Journal ID: ISSN 1532-0626
Publisher:
Wiley Blackwell (John Wiley & Sons)
Country of Publication:
United Kingdom
Language:
English

Citation Formats

Zhai, Keke, Banerjee, Tania, Zwick, David, Hackl, Jason, Koneru, Rahul, and Ranka, Sanjay. Dynamic load balancing for a mesh‐based scientific application. United Kingdom: N. p., 2020. Web. doi:10.1002/cpe.5626.
Zhai, Keke, Banerjee, Tania, Zwick, David, Hackl, Jason, Koneru, Rahul, & Ranka, Sanjay. Dynamic load balancing for a mesh‐based scientific application. United Kingdom. https://doi.org/10.1002/cpe.5626
Zhai, Keke, Banerjee, Tania, Zwick, David, Hackl, Jason, Koneru, Rahul, and Ranka, Sanjay. Mon . "Dynamic load balancing for a mesh‐based scientific application". United Kingdom. https://doi.org/10.1002/cpe.5626.
@article{osti_1581451,
title = {Dynamic load balancing for a mesh‐based scientific application},
author = {Zhai, Keke and Banerjee, Tania and Zwick, David and Hackl, Jason and Koneru, Rahul and Ranka, Sanjay},
abstractNote = {Summary CMT‐nek is a new scientific application for performing high fidelity predictive simulations of particle‐laden, explosively dispersed turbulent flows. CMT‐nek is compute‐intensive and targeted for deployment on exascale platforms. The moving particles are the primary source of load imbalance when the application is executed on parallel processors. In a demonstration problem, all the particles are initially in a closed container until a detonation occurs and the particles move apart. If all processors get an equal share of the fluid domain, then only some of the processors get sections of the domain that are initially laden with particles, leading to disparate loads on the processors. To eliminate load imbalance in different processors and to speed up the makespan, we present different load‐balancing algorithms for CMT‐nek on large‐scale multicore platforms. The load on a processor is determined using different techniques. The performance of the different load‐balancing algorithms is compared, and the associated overheads are analyzed. Evaluations of the application with and without load‐balancing are conducted, and these show that with load‐balancing, simulation time becomes faster by a factor of up to 9.97. The performance was further improved by a factor of up to 1.42 using machine‐learning–based algorithms.},
doi = {10.1002/cpe.5626},
journal = {Concurrency and Computation. Practice and Experience},
number = 9,
volume = 32,
place = {United Kingdom},
year = {2020},
month = {1}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
https://doi.org/10.1002/cpe.5626

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Cited by: 1 work
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