Education, Science, Technology, Innovation and Life
Open Access
Sign In

Research on Parallel Algorithm Optimization Strategies in High Performance Computing

Download as PDF

DOI: 10.23977/cpcs.2024.080101 | Downloads: 5 | Views: 163


Yi Zhan 1, Cennie Wang 1


1 Chengdu University, Chengdu, Sichuan, 610106, China

Corresponding Author

Yi Zhan


In the past decade, with the rapid growth of mobile internet, cloud computing, and big data technology, data has shown explosive growth in different fields. In the era of big data, people have more information to utilize, but the difficulty of obtaining effective information is also greater than before. Therefore, it is necessary to study parallel computing models and performance optimization for big data processing. Exploring the value behind big data using data processing techniques has become a current research focus in the field of data. Given the importance of parallel applications of artificial intelligence (AI) and big data, it is crucial to focus on analyzing the High Performance Computing (HPC) that integrates the two. The complexity and diversity of storage structures, computer architecture, as well as the large volume and complex data of big data processing problems, pose significant challenges for the application of high-performance computers in the field of big data processing. Big data not only provides AI with an increasingly rich set of training data, but also puts higher demands on the computing power of computer systems. Faced with the problems of large scale and complex computation of big data, this paper proposes a multi strategy parallel genetic algorithm (GA) based on machine learning (ML) for optimizing HPC.


High Performance Computing; Parallel algorithms; Optimization strategy


Yi Zhan, Cennie Wang, Research on Parallel Algorithm Optimization Strategies in High Performance Computing. Computing, Performance and Communication Systems (2024) Vol. 8: 1-6. DOI:


[1] Li Kenli, Yang Wangdong, Chen Cen, et al. High-performance computing for artificial intelligence and big data [J]. Frontier of data and computing growth, 2020, 2(1):11.
[2] Yan Hua, Wang Yisheng, Wang Ruiqi, et al. GPU-based parallel computing method for reliability of large-scale multi-stage mission system [J]. Systems engineering and electronics, 2019, 41(1):8.
[3] Yang Fan, Gao Guojing, Zhang Yifeng. Research on memory optimization algorithm of parallel computing framework [J]. Information Technology, 2020, 44(8):5.
[4] Lan Fengchong, Li Jiwen, Chen Jiqing. DG-SLAM Algorithm for Complex Deep Learning and Parallel Computing in Dynamic Scenes [J]. Journal of Jilin University: Engineering Edition, 2021, 51(4):10.
[5] Zhang Wenjie, Jiang Liehui. Big data clustering algorithm based on Map Reduce parallel computing [J]. Computer Application Research, 2020(1):4.
[6] Yang Yi, Xiong Ying. Simulation of multi-database parallel scheduling algorithm based on cloud computing platform [J]. Computer Simulation, 2023, 40(6):459-462.
[7] Shi X, Yu X, Esmaeili-Falak M. Improved arithmetic optimization algorithm and its application to carbon fiber reinforced polymer-steel bond strength estimation [J]. Composite Structures, 2023, 306(5):116599.
[8] Néstor Rocchetti, Nesmachnow S, Tancredi G. High-performance computing simulations of self-gravity in astronomical agglomerates:[J]. Simulation, 2023, 99(3):263-289.
[9] Yuan Xuefeng, Zhou Hua, Zhao Qi, et al. Research on NMSFast and Optimization of Pyramid Template Matching Algorithm [J]. Logistics and Fuzziness, 2023, 13(4):3994-4003.
[10] Xu Peiyu, Zhang Zeqiang, Guan Chao. Modeling and hybrid teaching optimization algorithm for the balancing problem of man-machine parallel disassembly line [J]. Computer Integrated Manufacturing System, 2023, 29(7): 2175-2190.

Downloads: 2074
Visits: 102530

Sponsors, Associates, and Links

All published work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright © 2016 - 2031 Clausius Scientific Press Inc. All Rights Reserved.