Bringing Compiling Databases to RISC Architectures

Ferdinand Gruber, Maximilian Bandle, Alexis Engelke, Thomas Neumann, Jana Giceva

Research output: Contribution to journalConference articlepeer-review

5 Scopus citations

Abstract

Current hardware development greatly influences the design decisions of modern database systems. For many modern performance-focused database systems, query compilation emerged as an integral part and different approaches for code generation evolved, making use of standard compilers, general-purpose compiler libraries, or domain-specific code generators. However, development primarily focused on the dominating x86-64 server architecture; but neglected current hardware developments towards other CPU architectures like ARM and other RISC architectures. Therefore, we explore the design space of code generation in database systems considering a variety of state-of-the-art compilation approaches with a set of qualitative and quantitative metrics. Based on our findings, we have developed a new code generator called FireARM for AArch64-based systems in our database system, Umbra. We identify general as well as architecture-specific challenges for custom code generation in databases and provide potential solutions to abstract or handle them. Furthermore, we present an extensive evaluation of different compilation approaches in Umbra on a wide variety of x86-64 and ARM machines. In particular, we compare quantitative performance characteristics such as compilation latency and query throughput. Our results show that using standard languages and compiler infrastructures reduces the barrier to employing query compilation and allows for high performance on big data sets, while domain-specific code generators can achieve a significantly lower compilation overhead and allow for better targeting of new architectures.

Original languageEnglish
Pages (from-to)1222-1234
Number of pages13
JournalProceedings of the VLDB Endowment
Volume16
Issue number6
DOIs
StatePublished - 2023
Event49th International Conference on Very Large Data Bases, VLDB 2023 - Vancouver, Canada
Duration: 28 Aug 20231 Sep 2023

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