Practical partial evaluation for high-performance dynamic language runtimes

Würthinger, Thomas; Wimmer, Christian; Humer, Christian; Wöß, Andreas; Mössenböck, Hanspeter; Seaton, Chris; Duboscq, Gilles; Simon, Doug; Grimmer, Matthias

doi:10.1145/3062341.3062381

Cited by 73 publications

(37 citation statements)

References 42 publications

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“…We implemented our record & replay solution for communicating event-loop actors in SOMns. SOMns is written in Java as a selfoptimizing abstract syntax tree (AST) interpreter [44] using the Truffle framework and Graal just-in-time compiler [43]. This allows us to integrate record & replay directly into the language implementation.…”

Section: Methodsmentioning

confidence: 99%

Efficient and deterministic record & replay for actor languages

Aumayr

Marr

Béra

et al. 2018

Proceedings of the 15th International Conference on Managed Languages &Amp; Runtimes - ManLang '18

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With the ubiquity of parallel commodity hardware, developers turn to high-level concurrency models such as the actor model to lower the complexity of concurrent software. However, debugging concurrent software is hard, especially for concurrency models with a limited set of supporting tools. Such tools often deal only with the underlying threads and locks, which obscures the view on e.g. actors and messages and thereby introduces additional complexity.To improve on this situation, we present a low-overhead record & replay approach for actor languages. It allows one to debug concurrency issues deterministically based on a previously recorded trace. Our evaluation shows that the average run-time overhead for tracing on benchmarks from the Savina suite is 10% (min. 0%, max. 20%). For Acme-Air, a modern web application, we see a maximum increase of 1% in latency for HTTP requests and about 1.4 MB/s of trace data. These results are a first step towards deterministic replay debugging of actor systems in production.

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Section: Methodsmentioning

confidence: 99%

Efficient and deterministic record & replay for actor languages

Aumayr

Marr

Béra

et al. 2018

Proceedings of the 15th International Conference on Managed Languages &Amp; Runtimes - ManLang '18

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“…To achieve high performance, Trule partially evaluates [30,31] the trees and dynamically compiles them to optimized machine code. For dynamic compilation it uses the Graal compiler [9, 10, 12, 24ś 26].…”

Section: Trufle and Graalmentioning

confidence: 99%

“…Furthermore, our approach avoids changes to the Java bytecode and the Java language itself, which makes it a completely transparent and backwardcompatible solution. 1 Project Valhalla, http:⁄⁄openjdk.java.net⁄projects⁄valhalla⁄ 1 To demonstrate our approach, we use Trule [30,31], a language implementation framework that was designed for the implementation of dynamically-typed languages on top of a JVM, and use it to implement a Java bytecode interpreter, which we call TruleBC. Our performance evaluation shows that it is possible to outperform state of the art JVMs by applying code and data optimizations designed for dynamically-typed languages to a statically-typed language such as Java.…”

Section: Introductionmentioning

confidence: 99%

Applying Optimizations for Dynamically-typed Languages to Java

Grimmer¹,

Marr

Kahlhofer

et al. 2017

Proceedings of the 14th International Conference on Managed Languages and Runtimes

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While Java is a statically-typed language, some of its features make it behave like a dynamically-typed language at run time. This includes Java's boxing of primitive values as well as generics, which rely on type erasure. This paper investigates how runtime technology for dynamically-typed languages such as JavaScript and Python can be used for Java bytecode. Using optimistic optimizations, we specialize bytecode instructions that access references in such a way, that they can handle primitive data directly and also specialize data structures in order to avoid boxing for primitive types. Our evaluation shows that these optimizations can be successfully applied to a statically-typed language such as Java and can also improve performance signiicantly. With this approach, we get an eicient implementation of Java's generics, avoid changes to the Java language, and maintain backwards compatibility, allowing existing code to beneit from our optimization transparently.

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“…Sulong is used in the multilingual GraalVM [7,18,25] to implement native function interfaces of dynamic languages such as TruffleRuby [2]. All these language implementations are based on a common language implementation framework, called Truffle [31]. It uses dynamic-compilation based on profiling information gathered during interpretation of abstract syntax trees (ASTs) for efficient execution of the implemented language.…”

Section: Introductionmentioning

confidence: 99%

Supporting on-stack replacement in unstructured languages by loop reconstruction and extraction

Mosaner¹,

Leopoldseder²,

Rigger

et al. 2019

Proceedings of the 16th ACM SIGPLAN International Conference on Managed Programming Languages and Runtimes

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On-stack replacement (OSR) is a common technique employed by dynamic compilers to reduce program warm-up time. OSR allows switching from interpreted to compiled code during the execution of this code. The main targets are long running loops, which need to be represented explicitly, with dedicated information about condition and body, to be optimized at run time. Bytecode interpreters, however, represent control flow implicitly via unstructured jumps and thus do not exhibit the required high-level loop representation. To enable OSR also for jump-based-often called unstructuredlanguages, we propose the partial reconstruction of loops in order to explicitly represent them in a bytecode interpreter. Besides an outline of the general idea, we implemented our approach in Sulong, a bytecode interpreter for LLVM bitcode, which allows the execution of C/C++. We conducted an evaluation with a set of C benchmarks, which showed speed-ups in warm-up of up to 9x for certain benchmarks. This facilitates execution of programs with long-running loops in rarely called functions, which would yield significant slowdown without OSR. While shown with a prototype implementation, the overall idea of our approach is generalizable for all bytecode interpreters.

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Practical partial evaluation for high-performance dynamic language runtimes

Cited by 73 publications

References 42 publications

Efficient and deterministic record & replay for actor languages

Efficient and deterministic record & replay for actor languages

Applying Optimizations for Dynamically-typed Languages to Java

Supporting on-stack replacement in unstructured languages by loop reconstruction and extraction

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