Energy Optimization of Memory Intensive Parallel workloads

TL;DR

This paper develops an analytical model for energy consumption in multicore processors that accounts for memory access and idle core energy, optimizing frequency settings for energy efficiency within performance constraints.

Contribution

It introduces a comprehensive energy-performance model for memory-intensive workloads that includes idle core energy and guides frequency scaling for energy minimization.

Findings

Model accurately predicts energy consumption including memory and idle core effects.

Framework effectively determines optimal frequencies for energy savings.

Scheduling criteria improve energy efficiency in memory-intensive applications.

Abstract

Energy consumption is an important concern in modern multicore processors. The energy consumed during the execution of an application can be minimized by tuning the hardware state utilizing knobs such as frequency, voltage etc. The existing theoretical work on energy mini- mization using Global DVFS (Dynamic Voltage and Frequency Scaling), despite being thorough, ignores the energy consumed by the CPU on memory accesses and the dynamic energy consumed by the idle cores. This article presents an analytical model for the performance and the overall energy consumed by the CPU chip on CPU instructions as well as the memory accesses without ignoring the dynamic energy consumed by the idle cores. We present an analytical framework around our energy-performance model to predict the operating frequencies for global DVFS that minimize the overall CPU energy consumption within a performance budget. Finally, we suggest a scheduling criteria for energy aware scheduling of memory intensive parallel applications.