research-article

Solving large numerical optimization problems in HPC with Python

Author:

Antonio Gómez-IglesiasAuthors Info & Claims

PyHPC '15: Proceedings of the 5th Workshop on Python for High-Performance and Scientific Computing

Article No.: 7, Pages 1 - 8

https://doi.org/10.1145/2835857.2835864

Published: 15 November 2015 Publication History

Abstract

Numerical optimization is a complex problem in which many different algorithms can be used. Distributed metaheuristics have received attention but they normally focus on small problems. Many large scientific problems can take advantage of these techniques to find optimal solutions for the problems. However, solving large scientific problems presents specific issues that traditional implementations of metaheuristics do not tackle. This research presents a large parallel optimization solver that uses Python to follow a generic model that can be easily extended with new algorithms. It also makes extensive use of NumPy for an efficient utilization of the computational resources and MPI4py for communication in HPC environments. The presented model has proven to be an excellent approach for solving very large problems in an efficient manner while using the computational resources in different HPC environments adequately.

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Cited By

Weßner JBerlich RSchwarz KLutz M(2023)Parametric Optimization on HPC Clusters with GenevaComputing and Software for Big Science10.1007/s41781-023-00098-67:1Online publication date: 21-Apr-2023
https://doi.org/10.1007/s41781-023-00098-6
Castejón FGómez-Iglesias ALópez-Fraguas AGaither K(2016)Optimization of 3D Fusion DevicesProceedings of the XSEDE16 Conference on Diversity, Big Data, and Science at Scale10.1145/2949550.2949646(1-7)Online publication date: 17-Jul-2016
https://dl.acm.org/doi/10.1145/2949550.2949646
Gómez-Iglesias AArora R(2016)Using High Performance Computing for Conquering Big DataConquering Big Data with High Performance Computing10.1007/978-3-319-33742-5_2(13-30)Online publication date: 17-Sep-2016
https://doi.org/10.1007/978-3-319-33742-5_2

Index Terms

Solving large numerical optimization problems in HPC with Python

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cover image ACM Conferences

PyHPC '15: Proceedings of the 5th Workshop on Python for High-Performance and Scientific Computing

November 2015

59 pages

ISBN:9781450340106

DOI:10.1145/2835857

Copyright © 2015 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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SIGHPC: ACM Special Interest Group on High Performance Computing, Special Interest Group on High Performance Computing
SIGARCH: ACM Special Interest Group on Computer Architecture
IEEE-CS\DATC: IEEE Computer Society

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Association for Computing Machinery

New York, NY, United States

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Published: 15 November 2015

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SC15: The International Conference for High Performance Computing, Networking, Storage and Analysis

November 15, 2015

Texas, Austin

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PyHPC '15 Paper Acceptance Rate 7 of 7 submissions, 100%;

Overall Acceptance Rate 7 of 7 submissions, 100%

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Cited By

Weßner JBerlich RSchwarz KLutz M(2023)Parametric Optimization on HPC Clusters with GenevaComputing and Software for Big Science10.1007/s41781-023-00098-67:1Online publication date: 21-Apr-2023
https://doi.org/10.1007/s41781-023-00098-6
Castejón FGómez-Iglesias ALópez-Fraguas AGaither K(2016)Optimization of 3D Fusion DevicesProceedings of the XSEDE16 Conference on Diversity, Big Data, and Science at Scale10.1145/2949550.2949646(1-7)Online publication date: 17-Jul-2016
https://dl.acm.org/doi/10.1145/2949550.2949646
Gómez-Iglesias AArora R(2016)Using High Performance Computing for Conquering Big DataConquering Big Data with High Performance Computing10.1007/978-3-319-33742-5_2(13-30)Online publication date: 17-Sep-2016
https://doi.org/10.1007/978-3-319-33742-5_2

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