Energy Aware Algorithmic Engineering
Energy Aware Algorithmic Engineering
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能源感知算法工程
DOI：
10.1109/mascots.2014.47
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
Akshat Verma
中科院分区：
文献类型：
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作者：
Swapnoneel Roy;A. Rudra;Akshat Verma
在这项工作中，我们认为能量管理应该成为算法设计和实现的指导原则。传统的算法复杂性模型过于简单，不利于节能算法的设计。在这项工作中，我们进行了大量的实验，以了解算法的能量消耗。我们研究了流行的向量运算、矩阵运算、排序和图算法的能量消耗。我们观察到，任何给定算法的能量消耗取决于该算法在RAM中对于给定数据布局可以表现出的存储器并行性，对于许多流行的算法来说，变化高达100%。我们的实验验证了文[1]中提出的渐近能量复杂性模型，并提出了许多实用的见解。我们发现，在能源方面，读取可能比写入更昂贵，并且不同的数据类型可能会导致不同的能源消耗。我们最重要的结果是从理论和实验上量化了并行数据序列对能源消耗的影响。我们还观察到，对于多个并发访问序列，高内存并行度也会增加能量消耗。我们利用从实验中获得的见解，为实用的节能软件提出算法工程技术。
In this work, we argue that energy management should be a guiding principle for design and implementation of algorithms. Traditional complexity models for algorithms are simple and do not aid in design of energy-efficient algorithms. In this work, we conducted a large number of experiments to understand energy consumption for algorithms. We study the energy consumption for popular vector operations, matrix operations, sorting, and graph algorithms. We observed that the energy consumption for any given algorithm depends on the memory parallelism the algorithm can exhibit for a given data layout in the RAM with variations up to 100% for many popular algorithms. Our experiments validate the asymptotic energy complexity model presented in a companion paper [1] and brings out many practical insights. We show that reads can be more expensive in terms of energy than writes, and different data types can lead to different energy consumption. Our most important result is a theoretical and experimental quantification of the impact of parallel data sequences on energy consumption. We also observe that high memory parallelism can also increase energy consumption with multiple concurrent access sequences. We use insights from our experiments to propose algorithmic engineering techniques for practical energy efficient software.