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Analysis of GPU-Libraries for Rapid Prototyping Database Operations : A look into library support for database operations

机译：用于快速原型数据库操作的GPU库的分析：查看数据库操作的库支持

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Using GPUs for query processing is still an ongoing research in the database community due to the increasing heterogeneity of GPUs and their capabilities (e.g., their newest selling point: tensor cores). Hence, many researchers develop optimal operator implementations for a specific device generation involving tedious operator tuning by hand. On the other hand, there is a growing availability of GPU libraries that provide optimized operators for manifold applications. However, the question arises how mature these libraries are and whether they are fit to replace handwritten operator implementations not only w.r.t. implementation effort and portability, but also in terms of performance.In this paper, we investigate various general-purpose libraries that are both portable and easy to use for arbitrary GPUs in order to test their production readiness on the example of database operations. To this end, we develop a framework to show the support of GPU libraries for database operations that allows a user to plug-in new libraries and custom-written code. Our experiments show that the tested GPU libraries (ArrayFire, Thrust, and Boost.Compute) do support a considerable set of database operations, but there is a significant diversity in terms of performance among libraries. Furthermore, one of the fundamental database primitives – hashing and, thus, hash joins – is currently not supported, leaving important tuning potential unused.
机译：由于GPU的异质性和其能力增加，使用GPU仍然是数据库社区的持续研究，以及它们的能力（例如，他们最新的卖点：张量核心）。因此，许多研究人员为涉及手工繁琐的操作员调整的特定设备生成开发最佳操作员实现。另一方面，GPU库的可用性不断增长，为歧管应用提供优化的运营商。但是，问题出现了这些库的成熟程度以及它们是否适合替换手写操作员实现不仅是w.r.t.实施努力和可移植性，也是在表现方面。在本文中，我们调查了各种通用库，这些库都是可便携式的，易于用于任意GPU，以便在数据库操作的示例上测试其生产准备情况。为此，我们开发了一个框架，以显示GPU库对数据库操作的支持，允许用户插入新库和自定义代码。我们的实验表明，测试的GPU库（Arrayfire，Thrust和Boost.Compute）确实支持相当大量的数据库操作，但在图书馆之间的性能方面存在显着的多样性。此外，目前不支持一个基本数据库基元 - 散列，因此，哈希联接 - 是不支持的，留下重要的调整潜在未使用。

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