Efficient generation of machine code for query compilers

Funke, Henning; Mühlig, Jan; Teubner, Jens

doi:10.1145/3399666.3399925

Cited by 10 publications

(2 citation statements)

References 13 publications

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“…One of the critical factors when (just in time) compiling code is register allocation. Funke et al (2020) showed an optimized approach by providing a lightweight intermediate representation that estimates value lifetimes before code generation. The Voodoo IR is a declarative algebra, especially for many-core architecture that provides a set of vectorization instructions to generate OpenCL code Pirk et al (2016).…”

Section: Low-level Intermediate Representationsmentioning

confidence: 99%

Adaptive Query Compilation in Graph Databases

Baumstark

Jibril

Sattler

2022

Preprint

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Compiling database queries into compact and efficient machine code has proven to be a great technique to improve query performance and to exploit characteristics of modern hardware. Particularly for graph database queries, which often execute the same instructions for processing, this technique can lead to an improvement. Furthermore, compilation frameworks like LLVM provide powerful optimization techniques and support different backends. However, the time for generating and optimizing machine code becomes an issue for short-running queries or queries which could produce early results quickly.In this work, we present an adaptive approach integrating graph query interpretation and compilation. While query compilation and code generation are running in the background, the query execution starts using the interpreter. As soon as the code generation is finished, the execution switches to the compiled code. Our evaluation shows that autonomously switching execution modes helps to hide compilation times and additionally the additional latencies of the underlying storage.

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Section: Low-level Intermediate Representationsmentioning

confidence: 99%

Adaptive Query Compilation in Graph Databases

Baumstark

Jibril

Sattler

2022

Preprint

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“…The Flying Start backend cannot do this, as its compiler is executed right ahead of it in the same compilation pipeline. Similarly, Flounder IR is a program representation that relies on the code generator to encode value lifetimes [9]. The proposed design for Flounder IR is to estimate values lifetimes with relational operator lifetimes.…”

Section: Related Workmentioning

confidence: 99%

Tidy Tuples and Flying Start: fast compilation and fast execution of relational queries in Umbra

Kersten¹,

Leis

Neumann³

2021

The VLDB Journal

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Although compiling queries to efficient machine code has become a common approach for query execution, a number of newly created database system projects still refrain from using compilation. It is sometimes claimed that the intricacies of code generation make compilation-based engines too complex. Also, a major barrier for adoption, especially for interactive ad hoc queries, is long compilation time. In this paper, we examine all stages of compiling query execution engines and show how to reduce compilation overhead. We incorporate the lessons learned from a decade of generating code in HyPer into a design that manages complexity and yields high speed. First, we introduce a code generation framework that establishes abstractions to manage complexity, yet generates code in a single fast pass. Second, we present a program representation whose data structures are tuned to support fast code generation and compilation. Third, we introduce a new compiler backend that is optimized for minimal compile time, and simultaneously, yields superior execution performance to competing approaches, e.g., Volcano-style or bytecode interpretation. We implemented these optimizations in our database system Umbra to show that it is possible to unite fast compilation and fast execution. Indeed, Umbra achieves unprecedentedly low query latencies. On small data sets, it is even faster than interpreter engines like DuckDB and PostgreSQL. At the same time, on large data sets, its throughput is on par with the state-of-the-art compiling system HyPer.

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Adaptive query compilation in graph databases

Baumstark

Jibril

Sattler

2023

Distrib Parallel Databases

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Compiling database queries into compact and efficient machine code has proven to be a great technique to improve query performance and exploit characteristics of modern hardware. Particularly for graph database queries, which often execute the exact instructions for processing, this technique can lead to an improvement. Furthermore, compilation frameworks like LLVM provide powerful optimization techniques and support different backends. However, the time for generating and optimizing machine code becomes an issue for short-running queries or queries which could produce early results quickly. In this work, we present an adaptive approach integrating graph query interpretation and compilation. While query compilation and code generation are running in the background, the query execution starts using the interpreter. When the code generation is finished, the execution switches to the compiled code. Our evaluation of the approach using short-running and complex queries show that autonomously switching execution modes helps to improve the runtime of all types of queries and additionally to hide compilation times and the additional latencies of the underlying storage.

show abstract

Efficient generation of machine code for query compilers

Cited by 10 publications

References 13 publications

Adaptive Query Compilation in Graph Databases

Adaptive Query Compilation in Graph Databases

Tidy Tuples and Flying Start: fast compilation and fast execution of relational queries in Umbra

Adaptive query compilation in graph databases

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