Energy consumption in compact integer vectors: A study case

TL;DR

This paper investigates the energy consumption of various compact integer vector representations, analyzing how factors like instructions and CPU cycles influence energy use beyond just execution time.

Contribution

It provides an initial analysis of energy consumption factors in compact data structures, emphasizing the importance of energy-aware design in green computing.

Findings

Energy consumption correlates with execution time but not exclusively.

Other factors like CPU cycles and memory loads significantly impact energy use.

Understanding these factors aids in designing energy-efficient data structures.

Abstract

In the field of algorithms and data structures analysis and design, most of the researchers focus only on the space/time trade-off, and little attention has been paid to energy consumption. Moreover, most of the efforts in the field of Green Computing have been devoted to hardware-related issues, being green software in its infancy. Optimizing the usage of computing resources, minimizing power consumption or increasing battery life are some of the goals of this field of research. As an attempt to address the most recent sustainability challenges, we must incorporate the energy consumption as a first-class constraint when designing new compact data structures. Thus, as a preliminary work to reach that goal, we first need to understand the factors that impact on the energy consumption and their relation with compression. In this work, we study the energy consumption required by several integer vector representations. We execute typical operations over datasets of different nature. We can see that, as commonly believed, energy consumption is highly related to the time required by the process, but not always. We analyze other parameters, such as number of instructions, number of CPU cycles, memory loads, among others.