Evaluation and Test for Scalability of Numerical Parallel Computation

Chi Lihua, Liu jie, and Hu Qingfeng

Journal of Computer Research and Development > 2005 > 42(6): 1073-1078.

Chi Lihua, Liu jie, and Hu Qingfeng. Evaluation and Test for Scalability of Numerical Parallel Computation[J]. Journal of Computer Research and Development, 2005, 42(6): 1073-1078.

Citation:

Chi Lihua, Liu jie, and Hu Qingfeng. Evaluation and Test for Scalability of Numerical Parallel Computation[J]. Journal of Computer Research and Development, 2005, 42(6): 1073-1078.

Citation:

Chi Lihua, Liu jie, and Hu Qingfeng. Evaluation and Test for Scalability of Numerical Parallel Computation[J]. Journal of Computer Research and Development, 2005, 42(6): 1073-1078.

PDF (302 KB)

Evaluation and Test for Scalability of Numerical Parallel Computation

Chi Lihua, Liu jie, and Hu Qingfeng

(School of Computer Science, National University of Defense Technology, Changsha 410073)

More Information

Published Date: June 14, 2005

Graphical Abstract

Abstract

Abstract

In this paper, the existing problems of the current scalability models are analyzed, and aiming at the requirements of the practical evaluations and tests, a practical scalability metric based on iso-average-computation-load is proposed to provide a quantitative measurement of the scalability. In this metric, the definitions of scalable speedup and scalability are different from the current metrics. By using this metric, a practical method can be obtained to test scalable speedup and scalability, and combined with curve fitting or parallel computing time model, the scalabilities of parallel systems can be predicted.
- numerical parallel computation,
- scalability,
- speedup,
- performance evaluation metric

FullText(HTML)

References (0)

[1]	Shi Leyi, Zhu Hongqiang, Liu Yihao, Liu Jia. Intrusion Detection of Industrial Control System Based on Correlation Information Entropy and CNN-BiLSTM[J]. Journal of Computer Research and Development, 2019, 56(11): 2330-2338. DOI: 10.7544/issn1000-1239.2019.20190376
[2]	Yao Sheng, Xu Feng, Zhao Peng, Ji Xia. Intuitionistic Fuzzy Entropy Feature Selection Algorithm Based on Adaptive Neighborhood Space Rough Set Model[J]. Journal of Computer Research and Development, 2018, 55(4): 802-814. DOI: 10.7544/issn1000-1239.2018.20160919
[3]	Dong Hongbin, Teng Xuyang, Yang Xue. Feature Selection Based on the Measurement of Correlation Information Entropy[J]. Journal of Computer Research and Development, 2016, 53(8): 1684-1695. DOI: 10.7544/issn1000-1239.2016.20160172
[4]	Tang Chenghua, Liu Pengcheng, Tang Shensheng, Xie Yi. Anomaly Intrusion Behavior Detection Based on Fuzzy Clustering and Features Selection[J]. Journal of Computer Research and Development, 2015, 52(3): 718-728. DOI: 10.7544/issn1000-1239.2015.20130601
[5]	Zhang Fengbin and Wang Tianbo. Real Value Negative Selection Algorithm with the n-Dimensional Chaotic Map[J]. Journal of Computer Research and Development, 2013, 50(7): 1387-1398.
[6]	Zhang Zhenhai, Li Shining, Li Zhigang, and Chen Hao. Multi-Label Feature Selection Algorithm Based on Information Entropy[J]. Journal of Computer Research and Development, 2013, 50(6): 1177-1184.
[7]	Zheng Liming, Zou Peng, Han Weihong, Li Aiping, Jia Yan. Traffic Anomaly Detection Using Multi-Dimensional Entropy Classification in Backbone Network[J]. Journal of Computer Research and Development, 2012, 49(9): 1972-1981.
[8]	Zhang Xiang, Deng Zhaohong, Wang Shitong, Choi Kupsze. Maximum Entropy Relief Feature Weighting[J]. Journal of Computer Research and Development, 2011, 48(6): 1038-1048.
[9]	Chen Shitao, Chen Guolong, Guo Wenzhong, and Liu Yanhua. Feature Selection of the Intrusion Detection Data Based on Particle Swarm Optimization and Neighborhood Reduction[J]. Journal of Computer Research and Development, 2010, 47(7): 1261-1267.
[10]	Hou Jian, Peng Jiayin, Zhang Yuzhuo, Zhang Chengyi. A Reverse Triple I Algorithm for Fuzzy Reasoning Based on Maximum Fuzzy Entropy Principle[J]. Journal of Computer Research and Development, 2006, 43(7): 1180-1185.