Abstract
Energy consumption is one of the major issues in today’s computer science, and an increasing number of scientific communities are interested in evaluating the tradeoff between time-to-solution and energy-to-solution. Despite, in the last two decades, computing which revolved around centralized computing infrastructures, such as supercomputing and data centers, the wide adoption of the Internet of Things (IoT) paradigm is currently inverting this trend due to the huge amount of data it generates, pushing computing power back to places where the data are generated—the so-called fog/edge computing. This shift towards a decentralized model requires an equivalent change in the software engineering paradigms, development environments, hardware tools, languages, and computation models for scientific programming because the local computational capabilities are typically limited and require a careful evaluation of power consumption. This paper aims to present how these concepts can be actually implemented in scientific software by presenting the state of the art of powerful, less power-hungry processors from one side and energy-aware tools and techniques from the other one.
Highlights
Information and communication technologies (ICT) play a fundamental role in supporting human activities for the global economic, social, and environmentally sustainable developments [1]
Energy consumption is one of the most relevant issues for present computing platforms, and this trend is expected to continue in the foreseeable future. is implies that the electricity bill increasingly dominates costs related to the running of applications and the consequent environmental pollution [2]
Is situation is evident for high-performance computing (HPC) infrastructures, where the sum of the energy bills over a supercomputer’s lifetime is comparable to the acquisition cost and represents one of the most relevant elements of the total cost of ownership [3]. is is because energy is used for computation and for cooling, communication, storage, and display [4]
Summary
Information and communication technologies (ICT) play a fundamental role in supporting human activities for the global economic, social, and environmentally sustainable developments [1]. Is scenario must be combined because in the past two decades, computing has been focused around centralized (and possibly complex [10]) infrastructures, but the wider diffusion of cyber-physical systems (CPSs) is currently inverting this trend, pushing computing power back to where data are generated In both cases, the energy consumption of telecommunication networks is very relevant [11]. E usage of low-power System-on-Chip (SoC) architectures for scientific (and industrial) applications is discussed in [23], intending to assess the tradeoff among time-to-solution, energy-to-solution, and economic aspects for both scientific and commercial purposes they can achieve in comparison to traditional server-grade architectures adopted in present infrastructures. Erefore, this work’s main goal is to present the most relevant available solutions for users interested in improving the energy consumption of scientific software focusing on computation. E structure of the paper is as follows: Section 2 presents hardware techniques and solutions for achieving energy-savvy processing, Section 3 discusses tools and methodologies for supporting developers in producing energy-aware software, while the last section concludes the paper
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