A Unit Testing Framework for Scientific Legacy Code

Zhuo Yao; Dali Wang; Jinyuan Sun; Dong Zhong

doi:10.1109/CSCI.2017.163

A Unit Testing Framework for Scientific Legacy Code

Zhuo Yao
, Dali Wang
, Jinyuan Sun
, Dong Zhong

Earth Systems Modeling

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

Large-scale scientific applications play important roles in supporting research. However, it is often very expensive and time-consuming to make changes to, maintain and evolve the scientific code due to its complexity and poor programming skills of researchers. Therefore, in order to visualize scientific code architecture to optimize software design, understand undocumented source code, and analyze software flow and functionality, we first introduce a unit testing framework (UTF). Then, because such infrastructure\rq s performance is very crucial in practical use since the scientific legacy applications simulate instances in a long period of time, we improve the UTF by applying Message Passing based Parallelization and parallel I/O operations. Furthermore, due to the scientific code has enormous state data and the I/O capacity on the server is limited, we apply in situ data analysis method to encounter fewer resource limitations, and adopt signal processing to greatly reduce data transfer. Last, we demonstrated the correctness and high-efficiency of our framework for legacy Earth model on Titan supercomputer.

Original language	English
Title of host publication	Proceedings - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017
Editors	Fernando G. Tinetti, Quoc-Nam Tran, Leonidas Deligiannidis, Mary Qu Yang, Mary Qu Yang, Hamid R. Arabnia
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	940-944
Number of pages	5
ISBN (Electronic)	9781538626528
DOIs	https://doi.org/10.1109/CSCI.2017.163
State	Published - Dec 4 2018
Event	2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017 - Las Vegas, United States Duration: Dec 14 2017 → Dec 16 2017

Publication series

Name	Proceedings - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017

Conference

Conference	2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017
Country/Territory	United States
City	Las Vegas
Period	12/14/17 → 12/16/17

Funding

This research was funded by the U.S. Department of Energy (DOE), Office of Science, Biological and Environmental Research (BER) program, and Advanced Scientific Computing Research (ASCR) program. This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725.

Keywords

In situ data analysis
Legacy scientific application
Titan
Unit testing framework
parallel

Access to Document

10.1109/CSCI.2017.163

Cite this

Yao, Z., Wang, D., Sun, J., & Zhong, D. (2018). A Unit Testing Framework for Scientific Legacy Code. In F. G. Tinetti, Q.-N. Tran, L. Deligiannidis, M. Q. Yang, M. Q. Yang, & H. R. Arabnia (Eds.), Proceedings - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017 (pp. 940-944). Article 8560922 (Proceedings - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CSCI.2017.163

Yao, Zhuo ; Wang, Dali ; Sun, Jinyuan et al. / A Unit Testing Framework for Scientific Legacy Code. Proceedings - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017. editor / Fernando G. Tinetti ; Quoc-Nam Tran ; Leonidas Deligiannidis ; Mary Qu Yang ; Mary Qu Yang ; Hamid R. Arabnia. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 940-944 (Proceedings - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017).

@inproceedings{dbdb1d8b216448c39f2fc2f4eeeac93c,

title = "A Unit Testing Framework for Scientific Legacy Code",

abstract = "Large-scale scientific applications play important roles in supporting research. However, it is often very expensive and time-consuming to make changes to, maintain and evolve the scientific code due to its complexity and poor programming skills of researchers. Therefore, in order to visualize scientific code architecture to optimize software design, understand undocumented source code, and analyze software flow and functionality, we first introduce a unit testing framework (UTF). Then, because such infrastructure\textbackslash{}rq s performance is very crucial in practical use since the scientific legacy applications simulate instances in a long period of time, we improve the UTF by applying Message Passing based Parallelization and parallel I/O operations. Furthermore, due to the scientific code has enormous state data and the I/O capacity on the server is limited, we apply in situ data analysis method to encounter fewer resource limitations, and adopt signal processing to greatly reduce data transfer. Last, we demonstrated the correctness and high-efficiency of our framework for legacy Earth model on Titan supercomputer.",

keywords = "In situ data analysis, Legacy scientific application, Titan, Unit testing framework, parallel",

author = "Zhuo Yao and Dali Wang and Jinyuan Sun and Dong Zhong",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017 ; Conference date: 14-12-2017 Through 16-12-2017",

year = "2018",

month = dec,

day = "4",

doi = "10.1109/CSCI.2017.163",

language = "English",

series = "Proceedings - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "940--944",

editor = "Tinetti, \{Fernando G.\} and Quoc-Nam Tran and Leonidas Deligiannidis and Yang, \{Mary Qu\} and Yang, \{Mary Qu\} and Arabnia, \{Hamid R.\}",

booktitle = "Proceedings - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017",

}

Yao, Z, Wang, D, Sun, J & Zhong, D 2018, A Unit Testing Framework for Scientific Legacy Code. in FG Tinetti, Q-N Tran, L Deligiannidis, MQ Yang, MQ Yang & HR Arabnia (eds), Proceedings - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017., 8560922, Proceedings - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017, Institute of Electrical and Electronics Engineers Inc., pp. 940-944, 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017, Las Vegas, United States, 12/14/17. https://doi.org/10.1109/CSCI.2017.163

A Unit Testing Framework for Scientific Legacy Code. / Yao, Zhuo; Wang, Dali; Sun, Jinyuan et al.
Proceedings - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017. ed. / Fernando G. Tinetti; Quoc-Nam Tran; Leonidas Deligiannidis; Mary Qu Yang; Mary Qu Yang; Hamid R. Arabnia. Institute of Electrical and Electronics Engineers Inc., 2018. p. 940-944 8560922 (Proceedings - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A Unit Testing Framework for Scientific Legacy Code

AU - Yao, Zhuo

AU - Wang, Dali

AU - Sun, Jinyuan

AU - Zhong, Dong

PY - 2018/12/4

Y1 - 2018/12/4

N2 - Large-scale scientific applications play important roles in supporting research. However, it is often very expensive and time-consuming to make changes to, maintain and evolve the scientific code due to its complexity and poor programming skills of researchers. Therefore, in order to visualize scientific code architecture to optimize software design, understand undocumented source code, and analyze software flow and functionality, we first introduce a unit testing framework (UTF). Then, because such infrastructure\rq s performance is very crucial in practical use since the scientific legacy applications simulate instances in a long period of time, we improve the UTF by applying Message Passing based Parallelization and parallel I/O operations. Furthermore, due to the scientific code has enormous state data and the I/O capacity on the server is limited, we apply in situ data analysis method to encounter fewer resource limitations, and adopt signal processing to greatly reduce data transfer. Last, we demonstrated the correctness and high-efficiency of our framework for legacy Earth model on Titan supercomputer.

AB - Large-scale scientific applications play important roles in supporting research. However, it is often very expensive and time-consuming to make changes to, maintain and evolve the scientific code due to its complexity and poor programming skills of researchers. Therefore, in order to visualize scientific code architecture to optimize software design, understand undocumented source code, and analyze software flow and functionality, we first introduce a unit testing framework (UTF). Then, because such infrastructure\rq s performance is very crucial in practical use since the scientific legacy applications simulate instances in a long period of time, we improve the UTF by applying Message Passing based Parallelization and parallel I/O operations. Furthermore, due to the scientific code has enormous state data and the I/O capacity on the server is limited, we apply in situ data analysis method to encounter fewer resource limitations, and adopt signal processing to greatly reduce data transfer. Last, we demonstrated the correctness and high-efficiency of our framework for legacy Earth model on Titan supercomputer.

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KW - Legacy scientific application

KW - Titan

KW - Unit testing framework

KW - parallel

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DO - 10.1109/CSCI.2017.163

M3 - Conference contribution

AN - SCOPUS:85060634885

T3 - Proceedings - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017

SP - 940

EP - 944

BT - Proceedings - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017

A2 - Tinetti, Fernando G.

A2 - Tran, Quoc-Nam

A2 - Deligiannidis, Leonidas

A2 - Yang, Mary Qu

A2 - Arabnia, Hamid R.

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017

Y2 - 14 December 2017 through 16 December 2017

ER -

Yao Z, Wang D, Sun J, Zhong D. A Unit Testing Framework for Scientific Legacy Code. In Tinetti FG, Tran QN, Deligiannidis L, Yang MQ, Yang MQ, Arabnia HR, editors, Proceedings - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017. Institute of Electrical and Electronics Engineers Inc. 2018. p. 940-944. 8560922. (Proceedings - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017). doi: 10.1109/CSCI.2017.163

A Unit Testing Framework for Scientific Legacy Code

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