AOA: Adaptive Overclocking Algorithm on CPU-GPU Heterogeneous Platforms.

Although 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 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 ® Xeon E5-2660 & NVIDIA ® Tesla K80; Intel ® Core™i9-10920X & NIVIDIA ® 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.