Meta-compilation of Baseline JIT Compilers with Druid
February 27, 2025 Β· Declared Dead Β· π The Art, Science, and Engineering of Programming
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Nahuel Palumbo, Guillermo Polito, StΓ©phane Ducasse, Pablo Tesone
arXiv ID
2502.20543
Category
cs.PL: Programming Languages
Citations
1
Venue
The Art, Science, and Engineering of Programming
Last Checked
4 months ago
Abstract
Virtual Machines (VMs) combine interpreters and just-in-time (JIT) compiled code to achieve good performance. However, implementing different execution engines increases the cost of developing and maintaining such solutions. JIT compilers based on meta-compilation cope with these issues by automatically generating optimizing JIT compilers. This leaves open the question of how meta-compilation applies to baseline JIT compilers, which improve warmup times by trading off optimizations. In this paper, we present Druid, an ahead-of-time automatic approach to generate baseline JIT compiler frontends from interpreters. Language developers guide meta-compilation by annotating interpreter code and using Druid's intrinsics. Druid targets the meta-compilation to an existing JIT compiler infrastructure to achieve good warm-up performance. We applied Druid in the context of the Pharo programming language and evaluated it by comparing an autogenerated JIT compiler frontend against the one in production for more than 10 years. Our generated JIT compiler frontend is 2x faster on average than the interpreter and achieves on average 0.7x the performance of the handwritten JIT compiler. Our experiment only required changes in 60 call sites in the interpreter, showing that our solution makes language VMs **easier to maintain and evolve in the long run**.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Programming Languages
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions
R.I.P.
π»
Ghosted
Glow: Graph Lowering Compiler Techniques for Neural Networks
R.I.P.
π»
Ghosted
Learnable Programming: Blocks and Beyond
R.I.P.
π»
Ghosted
Scenic: A Language for Scenario Specification and Scene Generation
R.I.P.
π»
Ghosted
Vandal: A Scalable Security Analysis Framework for Smart Contracts
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted