Green Streams for Data-Intensive Software
Thomas W. Bartenstein and Yu David Liu
SUNY Binghamton, USA
Track: Technical Research
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.