In a typical Internet-of-Things setting that involves scientific applications, a target computation can be evaluated in many different ways depending on the split of computations among various devices. On the one hand, different implementations (or algorithms)--equivalent from a mathematical perspective--might exhibit significant difference in terms of performance. On the other hand, some of the implementations are likely to show similar performance characteristics. In this paper, we focus on analyzing the performance of a given set of algorithms by clustering them into performance classes. To this end, we use a measurement-based approach to evaluate and score algorithms based on pair-wise comparisons; we refer to this approach as"Relative performance analysis". Each comparison yields one of three outcomes: one algorithm can be "better", "worse", or "equivalent" to another; those algorithms evaluating to have equivalent performance are merged into the same performance class. We show that our clustering methodology facilitates algorithm selection with respect to more than one metric; for instance, from the subset of equivalently fast algorithms, one could then select an algorithm that consumes the least energy on a certain device.

中文翻译：

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通过相对性能进行边缘科学计算的性能比较

在涉及科学应用的典型物联网环境中，可以根据各种设备之间的计算方式，以多种不同方式评估目标计算。一方面，从数学的角度来看，不同的实现方式（或算法）可能在性能方面表现出显着差异。另一方面，某些实现可能会显示相似的性能特征。在本文中，我们将重点放在给定算法集的性能上，方法是将它们聚类到性能类别中。为此，我们使用基于度量的方法基于成对比较对算法进行评估和评分；我们将这种方法称为“相对性能分析”。每次比较都会产生以下三种结果之一：一种算法可以是“