一种通用的基于SIMD的压缩算法加速方法
A General SIMD-based Approach to Accelerating Compression Algorithms
摘要：
压缩算法对于面向数据的任务非常重要，特别是在大数据时代。配备了强大的SIMD指令集的现代处理器为我们提供了实现更好压缩性能的机会。以前的研究表明，基于SIMD的优化可以成倍增加解码速度。在这些开创性的研究之后，我们提出了一种通用的方法来加速压缩算法。通过实例化的方法，我们已经开发了几种新的整数压缩算法，称为组简单，组方案，组AFOR，和组PFD，并实现了相应的矢量化版本。我们评估两个公共TREC数据集，维基百科数据集和Twitter数据集上的算法。我们基于SIMD的算法具有竞争力的压缩比和编码速度，在解码速度方面优于最先进的非矢量化算法。
英文摘要：
Compression algorithms are important for data oriented tasks, especially in the era of Big Data. Modern processors equipped with powerful SIMD instruction sets, provide us an opportunity for achieving better compression performance. Previous research has shown that SIMD-based optimizations can multiply decoding speeds. Following these pioneering studies, we propose a general approach to accelerate compression algorithms. By instantiating the approach, we have developed several novel integer compression algorithms, called Group-Simple, Group-Scheme, Group-AFOR, and Group-PFD, and implemented their corresponding vectorized versions. We evaluate the proposed algorithms on two public TREC datasets, a Wikipedia dataset and a Twitter dataset. With competitive compression ratios and encoding speeds, our SIMD-based algorithms outperform state-of-the-art non-vectorized algorithms with respect to decoding speeds.
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