Data management systems commonly use bitmap indices to increase the efficiency of querying scientific data. Bitmaps are usually highly compressible and can be queried directly using fast hardware-supported bitwise logical operations. The processing of bitmap queries is inherently parallel in structure, which suggests they could benefit from concurrent computer systems. In particular, bitmap-range queries offer a highly parallel computational problem, and the hardware features of graphics processing units (GPUs) offer an alluring platform for accelerating their execution.In this paper, we present four GPU algorithms and two CPU-based algorithms for the parallel execution of bitmap-range queries. We show that in 98.8% of our tests, using real and synthetic data, the GPU algorithms greatly outperform the parallel CPU algorithms. For these tests, the GPU algorithms provide up to 54.1 × speedup and an average speedup of 11.5× over the parallel CPU algorithms. In addition to enhancing performance, augmenting traditional bitmap query systems with GPUs to offload bitmap query processing allows the CPU to process other requests.

中文翻译：

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在大型数据集上并行加速CPU和GPU范围查询

数据管理系统通常使用位图索引来提高查询科学数据的效率。位图通常是高度可压缩的，可以使用硬件支持的快速按位逻辑操作直接查询。位图查询的处理本质上是并行的结构，这表明它们可以从并发计算机系统中受益。尤其是位图范围查询提供了高度并行的计算问题，图形处理单元（GPU）的硬件功能为加速其执行提供了诱人的平台。在本文中，我们提出了四种GPU算法和两种基于CPU的算法位图范围查询的并行执行。我们显示，在98.8％的测试中，使用真实和合成数据，GPU算法大大优于并行CPU算法。对于这些测试，与并行CPU算法相比，GPU算法可提供高达54.1倍的加速比和平均11.5倍的加速比。除了提高性能之外，还可以使用GPU扩展传统的位图查询系统以减轻位图查询处理的负担，从而使CPU可以处理其他请求。