Green Streams for data-intensive software
Green Streams for data-intensive software
复制标题
适用于数据密集型软件的 Green Streams
DOI：
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
Yu David Liu
中科院分区：
文献类型：
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作者：
Thomas Bartenstein;Yu David Liu
本文介绍了绿色流，一种新的解决方案，以解决一个关键的，但往往被忽视的数据密集型软件的属性：能源效率。绿色流是建立在两个关键的见解，数据密集型软件。首先，数据密集型软件的能源消耗与数据量和数据处理密切相关，这两者在流编程范式中都是自然抽象的;其次，如果流程序的数据处理组件以“平衡”的方式协调，则可以提高能源效率，就像参与工人协调步伐时运行效率最高的装配线一样。绿色流采用标准流编程模型，并应用动态电压和频率缩放（DVFS）来协调组件之间的数据处理速度，最终实现能源效率，而不会降低并行处理环境中的性能。绿色流的核心是一种新颖的基于约束的推理，用于抽象流程序内部数据流速率的内在关系，该推理使用线性规划来最小化处理组件的频率（因此能耗），同时仍保持最大输出数据流速率。对绿色流算法的核心算法进行了形式化描述，并证明了其最优性。在StreamIt框架上对绿色流的有效性进行了评估，初步结果表明，该方法可以平均节省28%的CPU能耗，性能提高7%。
This paper introduces Green Streams, a novel solution to address a critical but often overlooked property of data-intensive software: energy efficiency. Green Streams is built around two key insights into data-intensive software. First, energy consumption of data-intensive software is strongly correlated to data volume and data processing, both of which are naturally abstracted in the stream programming paradigm; Second, energy efficiency can be improved if the data processing components of a stream program coordinate in a “balanced” way, much like an assembly line that runs most efficiently when participating workers coordinate their pace. Green Streams adopts a standard stream programming model, and applies Dynamic Voltage and Frequency Scaling (DVFS) to coordinate the pace of data processing among components, ultimately achieving energy efficiency without degrading performance in a parallel processing environment. At the core of Green Streams is a novel constraint-based inference to abstract the intrinsic relationships of data flow rates inside a stream program, that uses linear programming to minimize the frequencies - hence the energy consumption - for processing components while still maintaining the maximum output data flow rate. The core algorithm of Green Streams is formalized, and its optimality is established. The effectiveness of Green Streams is evaluated on top of the StreamIt framework, and preliminary results show the approach can save CPU energy by an average of 28% with a 7% performance improvement.