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.

中文翻译：

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科学编程中节能计算的硬件和软件解决方案

能源消耗是当今计算机科学的主要问题之一，越来越多的科学界对评估解决时间和解决能源问题之间的权衡感兴趣。尽管在过去的二十年中，围绕集中式计算基础设施（例如超级计算和数据中心）进行的计算，物联网 (IoT) 范式的广泛采用目前正在扭转这一趋势，因为它产生了大量数据，将计算能力推回到产生数据的地方——所谓的雾/边缘计算。这种向分散模式的转变需要软件工程范式、开发环境、硬件工具、语言、和科学编程的计算模型，因为本地计算能力通常是有限的，需要仔细评估功耗。本文旨在通过从一方面介绍功能强大、耗电较少的处理器的最新技术水平以及另一方面介绍能量感知工具和技术，介绍如何在科学软件中实际实现这些概念。