论文标题
GPU的高性能过滤器
High-Performance Filters For GPUs
论文作者
论文摘要
过滤大约存储一组物品，同时将准确性交易以提高空间效率，并可以解决加速器（例如GPU）上的有限内存。但是，由于过滤器研究的大多数进步都集中在CPU上，因此缺乏高性能和功能丰富的GPU过滤器。 在本文中，我们探讨了过滤器的设计空间，其目标是为GPU开发大量平行，高性能和功能丰富的过滤器。我们在性能，可用性和支持的功能方面评估了各种过滤器设计，并确定了两个过滤器设计，这些设计在性能，功能和可用性方面提供了正确的权衡。 我们提出了两个新的基于GPU的过滤器TCF和GQF，可以在各种高性能数据分析应用程序中使用。 TCF是设置的会员过滤器，并支持更快的插入和查询，而GQF则支持计数，该计数以额外的性能成本进行。 GQF和TCF都提供了点和批量插入API，旨在利用GPU中的大规模并行性，而无需牺牲可用性和必要的特征。 TCF和GQF的$ 4.4 \ times $和$ 1.4 \ times $ $ $ $ $比我们的基准测试中的以前的GPU过滤器快，同时克服了当前GPU过滤器中性能和可用性的基本限制。
Filters approximately store a set of items while trading off accuracy for space-efficiency and can address the limited memory on accelerators, such as GPUs. However, there is a lack of high-performance and feature-rich GPU filters as most advancements in filter research has focused on CPUs. In this paper, we explore the design space of filters with a goal to develop massively parallel, high performance, and feature rich filters for GPUs. We evaluate various filter designs in terms of performance, usability, and supported features and identify two filter designs that offer the right trade off in terms of performance, features, and usability. We present two new GPU-based filters, the TCF and GQF, that can be employed in various high performance data analytics applications. The TCF is a set membership filter and supports faster inserts and queries, whereas the GQF supports counting which comes at an additional performance cost. Both the GQF and TCF provide point and bulk insertion API and are designed to exploit the massive parallelism in the GPU without sacrificing usability and necessary features. The TCF and GQF are up to $4.4\times$ and $1.4\times$ faster than the previous GPU filters in our benchmarks and at the same time overcome the fundamental constraints in performance and usability in current GPU filters.
