G-TADOC: Enabling Efficient GPU-Based Text Analytics without Decompression
G-TADOC: Enabling Efficient GPU-Based Text Analytics without Decompression
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DOI：
10.1109/icde51399.2021.00148
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发表时间：
2021-04
期刊：
影响因子：
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通讯作者：
Feng Zhang;Zaifeng Pan;Yanliang Zhou;Jidong Zhai;Xipeng Shen;O. Mutlu;Xiaoyong Du
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文献类型：
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作者：
Feng Zhang;Zaifeng Pan;Yanliang Zhou;Jidong Zhai;Xipeng Shen;O. Mutlu;Xiaoyong Du
事实证明，直接压缩文本分析（TADOC）是一种很有前途的大数据分析技术。GPU是数据分析系统中非常受欢迎的加速器。不幸的是，到目前为止，还没有工作表明如何利用GPU来加速TADOC。我们描述了G-TADOC，第一个直接在压缩上提供基于GPU的文本分析的框架，有效地在GPU上实现高效的文本分析，而无需解压缩输入数据。G-TADOC解决了三个主要挑战。首先，TADOC涉及大量的依赖关系，这使得在GPU上开发大规模并行性变得困难。我们开发了一种新的细粒度线程级的GPU线程的工作负载调度策略，分区严重依赖的负载自适应细粒度的方式。其次，在开发G-TADOC时，数千个GPU线程写入同一结果缓冲区会导致不一致性，而直接使用锁和原子操作会导致大量同步开销。我们在GPU上开发了一个具有线程安全数据结构的内存池来处理这些困难。第三，保持字之间的序列信息是无损压缩的必要条件。我们设计了一种序列支持策略，在保证序列信息的同时保持了GPU的高并行性，实验结果表明，G-TADOC的平均加速比是现有TADOC的31.1倍。
Text analytics directly on compression (TADOC) has proven to be a promising technology for big data analytics. GPUs are extremely popular accelerators for data analytics systems. Unfortunately, no work so far shows how to utilize GPUs to accelerate TADOC. We describe G-TADOC, the first framework that provides GPU-based text analytics directly on compression, effectively enabling efficient text analytics on GPUs without decompressing the input data.G-TADOC solves three major challenges. First, TADOC involves a large amount of dependencies, which makes it difficult to exploit massive parallelism on a GPU. We develop a novel fine-grained thread-level workload scheduling strategy for GPU threads, which partitions heavily-dependent loads adaptively in a fine-grained manner. Second, in developing G-TADOC, thousands of GPU threads writing to the same result buffer leads to inconsistency while directly using locks and atomic operations lead to large synchronization overheads. We develop a memory pool with thread-safe data structures on GPUs to handle such difficulties. Third, maintaining the sequence information among words is essential for lossless compression. We design a sequence-support strategy, which maintains high GPU parallelism while ensuring sequence information.Our experimental evaluations show that G-TADOC provides 31.1× average speedup compared to state-of-the-art TADOC.