Runtime Power Allocation Based on Multi-GPU Utilization in GAMESS  

作　　者：Masha Sosonkina Vaibhav Sundriyal Jorge Luis Galvez Vallejo Masha Sosonkina;Vaibhav Sundriyal;Jorge Luis Galvez Vallejo(Department of Electrical and Computer Engineering, Old Dominion University, Norfolk, USA;Department of Chemistry, Iowa State University, Ames, USA)

机构地区：[1]Department of Electrical and Computer Engineering, Old Dominion University, Norfolk, USA [2]Department of Chemistry, Iowa State University, Ames, USA

出　　处：《Journal of Computer and Communications》2022年第9期66-80,共15页电脑和通信（英文）

摘　　要：To improve the power consumption of parallel applications at the runtime, modern processors provide frequency scaling and power limiting capabilities. In this work, a runtime strategy is proposed to maximize performance under a given power budget by distributing the available power according to the relative GPU utilization. Time series forecasting methods were used to develop workload prediction models that provide accurate prediction of GPU utilization during application execution. Experiments were performed on a multi-GPU computing platform DGX-1 equipped with eight NVIDIA V100 GPUs used for quantum chemistry calculations in the GAMESS package. For a limited power budget, the proposed strategy may deliver as much as hundred times better GAMESS performance than that obtained when the power is distributed equally among all the GPUs.To improve the power consumption of parallel applications at the runtime, modern processors provide frequency scaling and power limiting capabilities. In this work, a runtime strategy is proposed to maximize performance under a given power budget by distributing the available power according to the relative GPU utilization. Time series forecasting methods were used to develop workload prediction models that provide accurate prediction of GPU utilization during application execution. Experiments were performed on a multi-GPU computing platform DGX-1 equipped with eight NVIDIA V100 GPUs used for quantum chemistry calculations in the GAMESS package. For a limited power budget, the proposed strategy may deliver as much as hundred times better GAMESS performance than that obtained when the power is distributed equally among all the GPUs.

关 键 词：Time Series Forecasting ARIMA Power Allocation Performance Modeling GAMESS GPU Utilization 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]