APPROACH FOR CODE ANALYSIS TO ESTIMATE POWER CONSUMPTION OF CUDA CORE - HURMAN, Ivan; BOBROVNIKOVA, Kira; BEDRATYUK, Leonid; BEDRATYUK, Hanna (Herald of Khmelnytskyi National University. Technical sciences, 2023)
The graphics processing unit is a popular computing device for achieving exascale performance in high-performance computing programs, which is used not only in graphics tasks, but also in computational tasks such as machine learning, scientific computing, and cryptography. With the help of a graphics processor, you can achieve significant speed and performance compared to the central processing unit. CUDA, Compute Unified Device Architecture, a graphics processing unit software development platform, allows developers to use the high-performance computing capabilities of graphics processing units to solve problems traditionally handled by central processing units.Even though the graphics processing unit has a relatively high power to performance ratio, it consumes a significant amount of power during computing. The paper proposes an approach for code analysis to estimate power consumption of CUDA core to improve the power efficiency of applications focused on computing on graphics processing units. The proposed approach makes it possible to estimate the power consumption of such applications without the need to run them on physical devices. The proposed approach is based on static analysis of the CUDA program and machine learning methods.To evaluate the effectiveness of the proposed approach, three graphics processing unit architectures were used: NVIDIA PASCAL, NVIDIA TURING, and NVIDIA AMPERE. The results of the experiments showed that for the NVIDIA AMPERE architecture, the proposed approach using decision trees makes it possible to achieve a determination coefficient of 0.9173. The results obtained confirm the effectiveness of the proposed code analysis method for estimating the power consumption of the CUDA core. This method can be useful for CUDA developers who want to improve the efficiency and power efficiency of their programs.
Keywords: GPU; NVIDIA; CUDA; power consumption; high-performance computing