Cluster optimization algorithm based on CPU and GPU hybrid architecture

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.

Automated summary

This paper integrates the Kriging proxy model and energy efficiency modeling method into a cluster optimization algorithm of CPU and GPU hybrid architecture, proposing a parallel computing model for large-scale CPU/GPU heterogeneous high-performance computing systems and providing algorithm optimization for CPU/GPU heterogeneous clusters. The experimental results show significant speedup ratios for constructing the Kriging proxy model and the search algorithm, demonstrating the high feasibility of this heterogeneous cluster optimization algorithm.