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.

We introduce RATE TYPES, a novel type system to reason about and optimize data-intensive programs. Built around stream languages, RATE TYPES performs static quantitative reasoning about stream rates-the frequency of data items in a stream being consumed, processed, and produced-a critical performance characteristic previously addressed by numerous experimental approaches but few foundational efforts. Even though streams are fundamentally dynamic, we find two essential concepts of stream rate control-throughput ratio and natural rate-are intimately related to the program structure itself and can be effectively reasoned about by a type system. RATE TYPES is proven sound over a time-aware and parallelism-aware operational semantics. The strong soundness result tolerates arbitrary schedules, and does not require any synchronization between stream filters. We further demonstrate the applications of RATE TYPES in energy-efficient computing and CPU allocation on multi-core architectures.

Abstract. We introduce RATE TYPES, a novel type system to reason about and optimize data-intensive programs. Built around stream languages, RATE TYPES performs static quantitative reasoning about stream rates -the frequency of data items in a stream being consumed, processed, and produced -a critical performance characteristic previously addressed by numerous experimental approaches but few foundational efforts. Even though streams are fundamentally dynamic, we find two essential concepts of stream rate control -throughput ratio and natural rate -are intimately related to the program structure itself and can be effectively reasoned about by a type system. RATE TYPES is proven sound over a time-aware and parallelism-aware operational semantics. The strong soundness result tolerates arbitrary schedules, and does not require any synchronization between stream filters. We further demonstrate the applications of RATE TYPES in energy-efficient computing and CPU allocation on multi-core architectures.