A Power-Aware, Self-Adaptive Macro Data Flow Framework

<em><strong>Abstract: </strong>The dataflow programming model has been extensively used as an effective solution to implement efficient parallel programming frameworks. However, the amount of resources allocated to the runtime support is usually fixed once by the programmer or the runtime, and kept static during the entire execution. While there are cases where such a static choice may be appropriate, other scenarios may require to dynamically change the parallelism degree during the application execution. In this paper we propose an algorithm for multicore shared memory platforms, that dynamically selects the optimal number of cores to be used as well as their clock frequency according to either the workload pressure or to explicit user requirements. We implement the algorithm for both structured and unstructured parallel applications and we validate our proposal over three real applications, showing that it is able to save a significant amount of power, while not impairing the performance and not requiring additional effort from the application programmer.</em>

This dataset contains the raw data of the experiments and the scripts used to plot them.

摘要：数据流编程模型（dataflow programming model）已被广泛用作实现高效并行编程框架的有效解决方案。然而，分配给运行时支持模块的资源总量通常由程序员或运行时系统一次性设定，并在整个程序执行周期内保持静态。尽管此类静态选择在部分场景下适用，但其他应用场景需在程序运行期间动态调整并行度。本文针对多核共享内存平台提出了一种动态调节算法，该算法可依据工作负载压力或明确的用户需求，动态确定最优的核心使用数量及其时钟频率。我们针对结构化与非结构化并行应用完成了该算法的实现，并在三个真实应用中验证了所提方案的有效性。实验结果表明，该方案能够在不损害程序性能、无需应用程序员付出额外工作量的前提下，节省可观的功耗。
本数据集包含实验原始数据及用于绘制实验结果的脚本。