大规模并行无损数据解压缩
Massively-Parallel Lossless Data Decompression
摘要：
如今，数据量呈指数级增长，存储成本高昂，这使得压缩对于大数据行业至关重要。虽然研究集中在高效压缩上，但快速解压缩对于重复读取压缩数据的分析查询至关重要。虽然解压缩可以通过将每个数据块分配给不同的进程来实现某种程度的并行化，但突破性的加速需要利用现代多核处理器和GPU的大规模并行性来进行块内的数据解压缩。我们提出了两种新的技术，以增加在解压缩的并行度。第一种技术利用GPU和SIMD架构的大规模并行性。第二个牺牲了一些压缩效率，以消除在解压缩过程中限制并行性的数据依赖性。我们评估这些技术的DEFLATE方案，称为膨胀，这是基于LZ77压缩和霍夫曼编码的解压。与几个基于多核CPU的库进行头对头比较，我们实现了2倍的速度提升，同时在压缩比相当的情况下实现了17%的节能。
英文摘要：
Today's exponentially increasing data volumes and the high cost of storage make compression essential for the Big Data industry. Although research has concentrated on efficient compression, fast decompression is critical for analytics queries that repeatedly read compressed data. While decompression can be parallelized somewhat by assigning each data block to a different process, break-through speed-ups require exploiting the massive parallelism of modern multi-core processors and GPUs for data decompression within a block. We propose two new techniques to increase the degree of parallelism during decompression. The first technique exploits the massive parallelism of GPU and SIMD architectures. The second sacrifices some compression efficiency to eliminate data dependencies that limit parallelism during decompression. We evaluate these techniques on the decompressor of the DEFLATE scheme, called Inflate, which is based on LZ77 compression and Huffman encoding. We achieve a 2X speed-up in a head-to-head comparison with several multi-core CPU-based libraries, while achieving a 17% energy saving with comparable compression ratios.
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