Power-Aware Characteristics of Matrix Operations on Multicores

Abstract

GPU accelerators are massively parallel in nature and tailored for processing numerically intensive high-performance computing applications. But most of the applications that involve heavy computations take excess time to process as the dataset gets larger and lead to more power consumption. Hence, among all the factors in sustainable computing that contribute to operartional costs of GPU, power and time management is one of the major challenging issue. This paper presents a methodology to reduce power consumption in GPUs, meanwhile keeping parallel execution of jobs as high as possible. To achieve this, a power and time aware framework is created by integrating TensorFlow library, InterPSS, and Dynamic Voltage Frequency Scaling (DVFS) techniques. For most of the scientific computing, matrix operations are considered as the fundamental building block. So, the performance, power consumption, and power efficiency of GPU for matrix applications are analyzed under proposed model. Experimental results reveal that proposed methodology substantially reduces peak power of GPUs by 20%, with improved speedup in execution time around 15%.