[2504.17460] A Lightweight Method for Generating Multi-Tier JIT Compilation Virtual Machine in a Meta-Tracing Compiler Framework

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Computer Science > Programming Languages

arXiv:2504.17460 (cs)

[Submitted on 24 Apr 2025 (v1), last revised 3 Jul 2025 (this version, v3)]

Title:A Lightweight Method for Generating Multi-Tier JIT Compilation Virtual Machine in a Meta-Tracing Compiler Framework

Authors:Yusuke Izawa, Hidehiko Masuhara, Carl Friedrich Bolz-Tereick<br>View a PDF of the paper titled A Lightweight Method for Generating Multi-Tier JIT Compilation Virtual Machine in a Meta-Tracing Compiler Framework, by Yusuke Izawa and 2 other authors

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Abstract:Meta-compiler frameworks, such as RPython and Graal/Truffle, generate high-performance virtual machines (VMs) from interpreter definitions. Although they generate VMs with high-quality just-in-time (JIT) compilers, they still lack an important feature that dedicated VMs (i.e., VMs that are developed for specific languages) have, namely \emph{multi-tier compilation}. Multi-tier compilation uses light-weight compilers at early stages and highly-optimizing compilers at later stages in order to balance between compilation overheads and code quality.

We propose a novel approach to enabling multi-tier compilation in the VMs generated by a meta-compiler framework. Instead of extending the JIT compiler backend of the framework, our approach drives an existing (heavyweight) compiler backend in the framework to quickly generate unoptimized native code by merely embedding directives and compile-time operations into interpreter definitions.

As a validation of the approach, we developed 2SOM, a Simple Object Machine with a two-tier JIT compiler based on RPython. 2SOM first applies the tier-1 threaded code generator that is generated by our proposed technique, then, to the loops that exceed a threshold, applies the tier-2 tracing JIT compiler that is generated by the original RPython framework. Our performance evaluation that runs a program with a realistic workload showed that 2SOM improved, when compared against an RPython-based VM, warm-up performance by 15\%, with merely a 5\% reduction in peak performance.

Comments:<br>ECOOP 2025. Fixed DOI

Subjects:

Programming Languages (cs.PL)

Cite as:<br>arXiv:2504.17460 [cs.PL]

(or<br>arXiv:2504.17460v3 [cs.PL] for this version)

https://doi.org/10.48550/arXiv.2504.17460

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arXiv-issued DOI via DataCite

Submission history<br>From: Yusuke Izawa [view email]<br>[v1]<br>Thu, 24 Apr 2025 11:51:28 UTC (750 KB)

[v2]<br>Sat, 26 Apr 2025 15:49:43 UTC (5,642 KB)

[v3]<br>Thu, 3 Jul 2025 10:02:51 UTC (479 KB)

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