Self-optimizing AST interpreters

Würthinger, Thomas; Wöß, Andreas; Mössenböck, Hanspeter; Duboscq, Gilles; Simon, Doug; Wimmer, Christian

doi:10.1145/2384577.2384587

Cited by 107 publications

(69 citation statements)

References 15 publications

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“…Research on optimizations ignored AST interpreters for a long time [6] and only recently, with Truffle [3] reconsidered them actively. However, for bytecode interpreters, a number of optimizations have been developed.…”

Section: Bytecode Interpretersmentioning

confidence: 99%

“…Truffle is a Java-based framework to implement efficient AST interpreters [3]. Based on the execution paths taken and the types observed, Truffle dynamically specializes the AST.…”

Section: Truffle: Self-optimizing Ast Inter-pretersmentioning

confidence: 99%

“…During the first execution of this AST node, the variable n contains an integer and, consequently, the variable read node can be specialized for reading integers. This is beneficial to avoid boxing [3]. The message send node can then be specialized based on the receiver's class, i. e., Integer in this case.…”

Section: Trufflesom: a Self-optimizing Sommentioning

confidence: 99%

“…Specifically, we study RPython [2], which is know as the implementation platform for PyPy, a fast Python implementation, as well as Truffle [3], which is a new language implementation framework for the JVM that also aims to enable efficient language implementations.…”

Section: Introductionmentioning

confidence: 99%

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Are We There Yet?: Simple Language Implementation Techniques for the 21st Century

2014

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Abstract-With the rise of domain-specific languages (DSLs), research in language implementation techniques regains importance. While DSLs can help to manage the domain's complexity, it is rarely affordable to build highly optimizing compilers or virtual machines, and thus, performance remains an issue. Ideally, one would implement a simple interpreter and still reach acceptable performance levels. RPython and Truffle are two approaches that promise to facilitate language implementation based on simple interpreters, while reaching performance of the same order of magnitude as highly optimizing virtual machines. In this case study, we compare the two approaches to identify commonalities, weaknesses, and areas for further research to improve their utility for language implementations.

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Section: Bytecode Interpretersmentioning

confidence: 99%

“…Truffle is a Java-based framework to implement efficient AST interpreters [3]. Based on the execution paths taken and the types observed, Truffle dynamically specializes the AST.…”

Section: Truffle: Self-optimizing Ast Inter-pretersmentioning

confidence: 99%

Section: Trufflesom: a Self-optimizing Sommentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Are We There Yet?: Simple Language Implementation Techniques for the 21st Century

2014

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“…We compare the performance of four object-oriented languages built on the same platform, namely the HotSpot JVM with the Graal just-in-time (JIT) compiler [16] and the Truffle language implementation framework [18]. We find that these implementations exhibit very similar performance for our set of 14 benchmarks.…”

Section: Introductionmentioning

confidence: 99%

Cross-language compiler benchmarking: are we fast yet?

MarrStefan¹,

DalozeBenoit²,

Mössenböck³

2016

SIGPLAN Not.

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Comparing the performance of programming languages is difficult because they differ in many aspects including preferred programming abstractions, available frameworks, and their runtime systems. Nonetheless, the question about relative performance comes up repeatedly in the research community, industry, and wider audience of enthusiasts.This paper presents 14 benchmarks and a novel methodology to assess the compiler effectiveness across language implementations. Using a set of common language abstractions, the benchmarks are implemented in Java, JavaScript, Ruby, Crystal, Newspeak, and Smalltalk. We show that the benchmarks exhibit a wide range of characteristics using language-agnostic metrics. Using four different languages on top of the same compiler, we show that the benchmarks perform similarly and therefore allow for a comparison of compiler effectiveness across languages. Based on anecdotes, we argue that these benchmarks help language implementers to identify performance bugs and optimization potential by comparing to other language implementations.

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Two decades of live coding and debugging of virtual machines through simulation

et al. 2020

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OpenSmalltalk-VM is a virtual machine (VM) for languages in the Smalltalk family (eg, Squeak and Pharo), which is itself written in a subset of Smalltalk that can easily be translated to C. VM development is done in Smalltalk, an activity we call "simulation." The production VM is then derived by translating the core VM code to C. As a result, two execution models coexist: simulation, where the Smalltalk code is executed on top of a Smalltalk VM, and production, where the same code is compiled to an executable through a C compiler. The whole VM execution can be simulated: the heap is represented as a huge byte array, the VM code is executed as Smalltalk, and the native code generated by the just-in-time (JIT) compiler is executed by a processor simulator. All the Smalltalk development tools, such as the debugger, are then available while simulating. In addition, in simulation, it is also possible to use debugging features such as single stepping in the machine code generated by the JIT compiler. The Smalltalk development tools combined with the simulation debugging features provide developers with a productive environment in which to extend and debug the VM. In this article, we detail the VM simulation infrastructure and report our experiences developing and debugging VM features within it such as the garbage collector and the JIT compiler.

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Self-optimizing AST interpreters

Cited by 107 publications

References 15 publications

Are We There Yet?: Simple Language Implementation Techniques for the 21st Century

Are We There Yet?: Simple Language Implementation Techniques for the 21st Century

Cross-language compiler benchmarking: are we fast yet?

Two decades of live coding and debugging of virtual machines through simulation

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