Author(s)

Yi Zhan ¹, Cennie Wang ¹

Affiliation(s)

¹ Chengdu University, Chengdu, Sichuan, 610106, China

Corresponding Author

Yi Zhan

ABSTRACT

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.

KEYWORDS

High Performance Computing; Parallel algorithms; Optimization strategy

CITE THIS PAPER

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: http://dx.doi.org/10.23977/cpcs.2024.080101.

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