Power Consumption Analysis of Parallel Algorithms on GPUs

TL;DR

This paper introduces a model and measurement protocol for assessing power consumption of elementary GPU operations, enabling energy prediction and improved task scheduling for energy-efficient GPU computing.

Contribution

It presents a novel energy measurement protocol and a predictive model for power consumption of GPU operations, aiding energy-efficient scheduling.

Findings

Developed a new energy measurement protocol for GPU operations

Created a model to predict energy needs of GPU programs

Facilitated better task scheduling based on energy consumption

Abstract

Due to their highly parallel multi-cores architecture, GPUs are being increasingly used in a wide range of computationally intensive applications. Compared to CPUs, GPUs can achieve higher performances at accelerating the programs' execution in an energy-efficient way. Therefore GPGPU computing is useful for high performance computing applications and in many scientific research fields. In order to bring further performance improvements, GPU clusters are increasingly adopted. The energy consumed by GPUs cannot be neglected. Therefore, an energy-efficient time scheduling of the programs that are going to be executed by the parallel GPUs based on their deadline as well as the assigned priorities could be deployed to face their energetic avidity. For this reason, we present in this paper a model enabling the measure of the power consumption and the time execution of some elementary operations running on a single GPU using a new developed energy measurement protocol. Consequently, using our methodology, energy needs of a program could be predicted, allowing a better task scheduling.