Parallelizing Stream Compression for IoT Applications on Asymmetric Multicores

Xianzhi Zeng, Shuhao Zhang

Abstract: Data stream compression attracts much attention recently due to the rise of IoT applications. Thanks to the balanced computational power and energy consumption, asymmetric multicores are widely used in IoT devices. This paper introduces CStream, a novel framework for parallelizing stream compression on asymmetric multicores to minimize energy consumption without violating the user-specified compressing latency constraint. Existing works cannot effectively utilize asymmetric multicores for stream compression, primarily due to the non-trivial asymmetric computation and asymmetric communication effects. To this end, CStream is developed with the following two novel designs: 1) fine-grained decomposition, which decomposes a stream compression procedure into multiple fine-grained tasks to better expose the task-core affinities under the asymmetric computation effects; and 2) asymmetry-aware task scheduling, which schedules the decomposed tasks based on a novel cost model to exploit the exposed task-core affinities while considering asymmetric communication effects. To validate our proposal, we evaluate CStream with five competing mechanisms of parallelizing stream compression algorithms on a recent asymmetric multicore processor. We evaluate CStream with five competing mechanisms of parallelizing stream compression algorithms to validate our proposal on a recent asymmetric multicore processor. Our extensive experiments based on a benchmark of three algorithms and four datasets show that CStream outperforms alternative approaches by up to 53% lower energy consumption without compressing latency constraint violation.