Paradigm Shift in Big Data SuperComputing: DataFlow vs. ControlFlow
Paradigm Shift in Big Data SuperComputing: DataFlow vs. ControlFlow
复制标题
大数据超级计算的范式转变：数据流与控制流
作者：
N. Trifunovic;V. Milutinovic;Jakob Salom;A. Kos
本文讨论了大数据问题和应用程序的计算范式和编程模型的转变。我们比较数据流和控制流编程模型，通过它们的数量和质量方面。适合在DataFlow计算机上实现的大数据问题和应用程序不应使用与ControlFlow计算机相同的测量方法进行测量。我们提出了一种新的基准测试方法，它不仅考虑到执行时间，而且还考虑到完成任务所需的功率和空间。最近的研究表明，如果TOP500排名是基于新的性能指标，DataFlow机器将优于ControlFlow机器。为了支持上述主张，我们提出了八个最近实现的各种算法使用的DataFlow范式，这表明相当大的速度，降低功耗和节省空间的实现使用的ControlFlow范式。
The paper discusses the shift in the computing paradigm and the programming model for Big Data problems and applications. We compare DataFlow and ControlFlow programming models through their quantity and quality aspects. Big Data problems and applications that are suitable for implementation on DataFlow computers should not be measured using the same measures as ControlFlow computers. We propose a new methodology for benchmarking, which takes into account not only the execution time, but also the power and space, needed to complete the task. Recent research shows that if the TOP500 ranking was based on the new performance measures, DataFlow machines would outperform ControlFlow machines. To support the above claims, we present eight recent implementations of various algorithms using the DataFlow paradigm, which show considerable speed-ups, power reductions and space savings over their implementation using the ControlFlow paradigm.