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Cluster optimization algorithm based on CPU and GPU hybrid architecture

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Abstract

With the rapid development of network technology and parallel computing, clusters formed by connecting a large number of PCs with high-speed networks have gradually replaced the status of supercomputers in scientific research and production and high-performance computing with cost-effective advantages. The research purpose of this paper is to integrate the Kriging proxy model method and energy efficiency modeling method into a cluster optimization algorithm of CPU and GPU hybrid architecture. This paper proposes a parallel computing model for large-scale CPU/GPU heterogeneous high-performance computing systems, which can effectively describe the computing capabilities and various communication behaviors of CPU/GPU heterogeneous systems, and finally provide algorithm optimization for CPU/GPU heterogeneous clusters. According to the GPU architecture, an efficient method of constructing a Kriging proxy model and an optimized search algorithm are designed. The experimental results in this paper show that the construction of the Kriging proxy model can obtain a 220 times speedup ratio, and the search algorithm can reach an 8 times speedup ratio. It can be seen that this heterogeneous cluster optimization algorithm has high feasibility.

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  1. College of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081, China

    Fei Yin & Feng Shi

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  1. Fei Yin
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  2. Feng Shi
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Correspondence to Fei Yin.

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Yin, F., Shi, F. Cluster optimization algorithm based on CPU and GPU hybrid architecture. Cluster Comput 25, 2601–2611 (2022). https://doi.org/10.1007/s10586-021-03398-x

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