Out-of-the-box library support for DBMS operations on GPUs

Abstract

GPU accelerated query execution is still ongoing research in the database community, as GPUs continue to be heterogeneous in their architectures varying their capabilities (e.g., their newest selling point: tensor cores). Hence, many researchers come up with optimal operator implementations for a specific device generation involving tedious operator tuning by hand. Alternatively, there is a growing availability of GPU libraries providing optimized operators for various applications. However, the question arises of 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 performance. In this paper, we investigate various general-purpose libraries that are both portable and easy to use for arbitrary GPUs 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 framework allows for easy pluggability of new libraries for query execution using a simple task model. Using this framework, we develop multiple libraries (ArrayFire, Thrust, and boost.compute) supporting many database operations. We use these libraries to experiment with different devices to see the impact of the underlying device. Based on our experiments, we see 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.