AOA: Adaptive Overclocking Algorithm on CPU-GPU Heterogeneous Platforms

Author:

Ou Zhixin,Chen Juan,Sun Yuyang,Xu Tao,Jiang Guodong,Tan Zhengyuan,Qi Xinxin

Abstract

AbstractAlthough GPUs have been used to accelerate various convolutional neural network algorithms with good performance, the demand for performance improvement is still continuously increasing. CPU/GPU overclocking technology brings opportunities for further performance improvement in CPU-GPU heterogeneous platforms. However, CPU/GPU overclocking inevitably increases the power of the CPU/GPU, which is not conducive to energy conservation, energy efficiency optimization, or even system stability. How to effectively constrain the total energy to remain roughly unchanged during the CPU/GPU overclocking is a key issue in designing adaptive overclocking algorithms. There are two key factors during solving this key issue. Firstly, the dynamic power upper bound must be set to reflect the real-time behavior characteristics of the program so that algorithm can better meet the total energy unchanging constraints; secondly, instead of independently overclocking at both CPU and GPU sides, coordinately overclocking on CPU-GPU must be considered to adapt to real-time load balance for higher performance improvement and better energy constraints. This paper proposes an Adaptive Overclocking Algorithm (AOA) on CPU-GPU heterogeneous platforms to achieve the goal of performance improvement while the total energy remains roughly unchanged. AOA uses the function $$F_k$$ F k to describe the variable power upper bound and introduces the load imbalance factor W to realize the CPU-GPU coordinated overclocking. Through the verification of several types convolutional neural network algorithms on two CPU-GPU heterogeneous platforms (Intel$$^\circledR $$ ® Xeon E5-2660 & NVIDIA$$^\circledR $$ ® Tesla K80; Intel$$^\circledR $$ ® Core™i9-10920X & NIVIDIA$$^\circledR $$ ® GeForce RTX 2080Ti), AOA achieves an average of 10.7% performance improvement and 4.4% energy savings. To verify the effectiveness of the AOA, we compare AOA with other methods including automatic boost, the highest overclocking and static optimal overclocking.

Publisher

Springer Nature Switzerland

Reference38 articles.

1. OL. http://dag.wiee.rs/home-made/dstat/. Accessed Dec 2021

2. Linux kernel profiling with perf. OL. https://perf.wiki.kernel.org/index.php/Tutorial. Accessed Dec 2021

3. Nvidia system management interface. OL. https://developer.nvidia.com/nvidia-system-management-interface. Accessed Dec 2021

4. Abadi, M., et al.: Tensorflow: large-scale machine learning on heterogeneous distributed systems. arXiv abs/1603.04467 (2016)

5. Acun, B., Miller, P., Kale, L.V.: Variation among processors under turbo boost in HPC systems. In: Proceedings of the 2016 International Conference on Supercomputing. ICS 2016. Association for Computing Machinery, New York (2016). https://doi.org/10.1145/2925426.2926289, https://doi-org-s.nudtproxy.yitlink.com/10.1145/2925426.2926289

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