CPU and GPU hybrid cluster architecture system for deep learning

Abstract

The invention discloses a CPU and GPU hybrid cluster architecture system for deep learning. The system comprises a central processing unit CPU cluster used for operating a logic-intensive deep learning application; a graphics processing unit GPU cluster used for operating a computing-intensive deep learning application; a first switch connected with the CPU cluster; a second switch connected with the GPU cluster; a third switch connected with the first switch and the second switch; and a parallel storage device connected with the third switch and used for providing shared data for the CPU cluster and the GPU cluster. The system reduces resource power consumption, improves the deep learning processing efficiency and reduces the cost.

Description

technical field [0001] The invention relates to the technical field of Internet high-performance computing, in particular to a deep learning-oriented mixed CPU and GPU cluster architecture system. Background technique [0002] In 2006, Geoffrey Hinton, a professor at the University of Toronto in Canada and a leader in the field of machine learning, and his student Ruslan Salakhutdinov published an article in the top academic journal "Science", which opened a wave of deep learning in academia and industry. Since 2006, deep learning has continued to gain momentum in academia. Stanford University, New York University, University of Montreal, Canada, etc. have become important centers for deep learning research. In 2010, the DARPA program of the U.S. Department of Defense funded deep learning projects for the first time, and the participants included Stanford University, New York University and NEC American Research Institute. An important basis for supporting deep learning is...

AI Technical Summary

Problems solved by technology

[0004] At present, large-scale deep learning systems are composed of pure CPU cluster architecture, or pure GPU cluster architecture, but the pure CPU cluster architecture system is completely composed of CPU, which consumes a lot of CPU energy consumption, and the resource consumption is too large. Moreover, the CPU cluster can only handle deep learning of one application feature, and cannot handle other types of deep learning applications. The efficiency of processing deep learning applications is too low, and the hardware cost of the CPU itself is very high, and the cost of the entire CPU cluster is too high.

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