In the burgeoning realm of Internet of Things (IoT) applications on edge devices, data stream compression has become increasingly pertinent. The integration of added compression overhead and limited hardware resources on these devices calls for a nuanced software-hardware co-design. This paper introduces CStream, a pioneering framework crafted for parallelizing stream compression on multicore edge devices. CStream grapples with the distinct challenges of delivering a high compression ratio, high throughput, low latency, and low energy consumption. Notably, CStream distinguishes itself by accommodating an array of stream compression algorithms, a variety of hardware architectures and configurations, and an innovative set of parallelization strategies, some of which are proposed herein for the first time. Our evaluation showcases the efficacy of a thoughtful co-design involving a lossy compression algorithm, asymmetric multicore processors, and our novel, hardware-conscious parallelization strategies. This approach achieves a 2.8x compression ratio with only marginal information loss, 4.3x throughput, 65% latency reduction and 89% energy consumption reduction, compared to designs lacking such strategic integration.

翻译：在边缘设备物联网应用快速发展的背景下，数据流压缩变得日益重要。由于压缩算法的额外开销与设备有限硬件资源的矛盾，需要对软件与硬件进行协同设计。本文提出CStream，一个面向多核边缘设备流式压缩并行化的开创性框架。CStream旨在应对高压缩比、高吞吐量、低延迟和低能耗等多重挑战。值得注意的是，CStream通过支持多种流式压缩算法、多样化的硬件架构与配置，以及一系列创新的并行化策略（部分策略为本文首次提出）而独具特色。实验评估表明，通过采用有损压缩算法、非对称多核处理器以及新型硬件感知并行化策略的协同设计，相较于未进行此类战略集成的方案，本方法实现了2.8倍压缩比（仅产生极小信息损失）、4.3倍吞吐量、65%延迟降低和89%能耗降低。