High-Level and Efficient Stream Parallelism on Multi-core Systems with SPar for Data Compression Applications

The stream processing domain is present in several real-world applications that are running on multi-core systems. In this paper, we focus on data compression applications that are an important sub-set of this domain. Our main goal is to assess the programmability and efficiency of domain-specific language called SPar. It was specially designed for expressing stream parallelism and it promises higher-level parallelism abstractions without significant performance losses. Therefore, we parallelized Lzip and Bzip2 compressors with SPar and compared with state-of-the-art frameworks. The results revealed that SPar is able to efficiently exploit stream parallelism as well as provide suitable abstractions with less code intrusion and code re-factoring.流处理（stream processing）领域广泛存在于各类运行于多核系统的实际应用场景中。本文聚焦于该领域的重要子集——数据压缩应用。我们的核心研究目标为评估名为SPar的领域特定语言（domain-specific language）的可编程性与运行效率。该语言专为表达流并行性而设计，可在几乎不造成显著性能损失的前提下，提供更高层级的并行抽象能力。为此，我们使用SPar对Lzip与Bzip2两款压缩工具完成并行化改造，并与当前顶尖的并行框架开展对比实验。实验结果表明，SPar不仅可高效挖掘流并行性，还能够以更低的代码侵入量与代码重构成本，提供适配的并行抽象能力。