Ai BCS: A GPU cluster scheduling optimization based on SKE model

Abstract

A GPGPU is very important technology and a research hotspot for cloud computing. We pay close attention to its energy consumption and performance. In this paper, a static performance analysis model of GPU, SKE (Single Kernel Estimate), is set up to analyze the completion time of the kernel function on a GPU to find the optimal parallel solution to different tasks in a specific GPU device and the granularity size of the thread-block division, thus enabling the fastest execution speed of the kernel function. The deviation between the completion time calculated by SKE and the real execution time of the kernel is no more than 13%. On this basis, we calculate the completion time for each sub-GPU task and seek the critical path of the GPU cluster, and propose a GPU cluster scheduling algorithm, BCS (Based on Critical-path-Scheduling). The algorithm regulates the frequency of non-critical nodes, mainly through dynamic voltage and frequency scaling (DVFS) technology, and achieves the goal of reducing the energy consumption of GPU nodes without affecting the final completion time of the cluster. The evaluation results show that BCS reduces energy consumption by a maximum of 9.4%, compared to DRS.