Trace-based just-in-time type specialization for dynamic languages

Gal, Andreas; Eich, Brendan; Shaver, Mike; Anderson, David F.; Mandelin, David; Haghighat, Mohammad R.; Kaplan, Blake; Hoare, Graydon; Zbarsky, Boris; Orendorff, Jason; Ruderman, Jesse; Smith, Edwin; Reitmaier, Rick; Bebenita, Michael; Chang, Mason; Franz, Michael

doi:10.1145/1543135.1542528

Cited by 79 publications

(84 citation statements)

References 8 publications

(2 reference statements)

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“…A lack of formal benchmark sets for scripting languages make these suites a common source of benchmarking for scripting language implementations [10,3,6,17].…”

Section: Methodsmentioning

confidence: 99%

“…Recent research has shown the advantages of type specialization in JIT compilation [19,10]. However, little research exists on the specialization of interpreters for scripting languages.…”

Section: Motivationmentioning

confidence: 99%

“…functions, loops or traces). Optimization and specialization of a region of code can be based on: (1) analysis performed at JIT time [17] or (2) recording a single run of execution and compiling based on behavior found during that execution [10]. Future executions of the same region of code may or may not follow the same type and control flow as the compiled region and accordingly will remain in native code or request the compilation of further paths.…”

Section: Scope For Optimizationmentioning

confidence: 99%

“…This typically adds a significant overhead to executing programs in dynamically typed languages. A goal of much research is to reduce or eliminate dynamic type checks in compilers or virtual machines for dynamically typed languages [3,10,19,17]. The Lua VM supports nine variable types: NIL, BOOLEAN, LIGHTUSERDATA, NUMBER, STRING, TABLE, FUNCTION, THREAD, USERDATA.…”

Section: Introductionmentioning

confidence: 99%

“…Performing these type checks forms a significant portion of program execution. These costs seem unnecessary especially when we realize the same variable types are inevitably rechecked as loop iterations repeat the execution of the same block of code.Recent research has shown the advantages of type specialization in JIT compilation [19,10]. However, little research exists on the specialization of interpreters for scripting languages.…”

mentioning

confidence: 99%

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Dynamic interpretation for dynamic scripting languages

Williams

McCandless

Gregg

2010

Proceedings of the 8th Annual IEEE/ACM International Symposium on Code Generation and Optimization

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Dynamic scripting languages offer programmers increased flexibility by allowing properties of programs to be defined at run-time. Typically, program execution begins with an interpreter where type checks are implemented using conditional statements. Recent JIT compilers have begun removing run-time checks by specializing native code to program properties discovered at JIT time.This paper presents a novel intermediate representation for scripting languages that explicitly encodes types of variables. The dynamic representation is a flow graph, where each node is a specialized virtual instruction and each edge directs program flow based on control and type changes in the program. The interpreter thus performs specialized execution of whole programs. We present techniques for the efficient interpretation of our representation showing speedups of greater than 2x over static interpretation, with an average speedup of approximately 1.3x. MotivationScripting language virtual machines (VMs) typically compile high-level source code into an array of low-level opcodes (bytecodes). They then use a standard interpretation loop to execute the array of opcodes. Run-time type checks are implemented with either conditional statements or switch statements. Performing these type checks forms a significant portion of program execution. These costs seem unnecessary especially when we realize the same variable types are inevitably rechecked as loop iterations repeat the execution of the same block of code.Recent research has shown the advantages of type specialization in JIT compilation [19,10]. However, little research exists on the specialization of interpreters for scripting languages. We propose a scripting language representation and interpretation technique that performs specialized execution of dynamic code. We present a dynamic intermediate representation (DIR) which explicitly encodes the types of variables at each point of execution. The DIR is a flow graph, where each node is a specialized virtual instruction and each edge directs program flow based on control and type changes in the program. The proposed DIR therefore has a specialized path in the graph for every sequence of control and type changes found during execution.In this paper we present the initial development of our prototype implementation in the Lua Virtual Machine [12,13]. We present the design of our dynamic representation as well as the techniques used for its efficient interpretation. We illustrate our representation with a motivating example and present our plans for future interpreter optimizations. We compare the interpretation of our representation to the standard Lua implementation and show early speedups reaching a max of 2.13x and an average of 1.3x. We present reductions in the total number of instructions executed including reductions in conditional branches (used in interpreter type checking) as well as decreases in data and instruction cache accesses.

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“…A lack of formal benchmark sets for scripting languages make these suites a common source of benchmarking for scripting language implementations [10,3,6,17].…”

Section: Methodsmentioning

confidence: 99%

“…Recent research has shown the advantages of type specialization in JIT compilation [19,10]. However, little research exists on the specialization of interpreters for scripting languages.…”

Section: Motivationmentioning

confidence: 99%

Section: Scope For Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Dynamic interpretation for dynamic scripting languages

Williams

McCandless

Gregg

2010

Proceedings of the 8th Annual IEEE/ACM International Symposium on Code Generation and Optimization

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Smart selection of optimizations in dynamic compilers

Rosário

Silva

Camacho

et al. 2020

Concurrency and Computation

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Summary Dynamic compilers perform compilation and generation of target code during runtime, implying that the compilation time is added into the program runtime. Thus, to build a high‐performing dynamic compilation system, it is crucial to be able to generate high‐quality code and, at the same time, have a small compilation cost. In this article, we present an approach that uses machine learning to select sequences of optimization for dynamic compilation that considers both code quality and compilation overhead. Our approach starts by training a model, offline, with a knowledge bank of those sequences with low overhead and high‐quality code generation capability using a genetic heuristic. Then, this bank is used to guide the smart selection of optimizations sequences for the compilation of code fragments during the emulation of an application. We evaluate the proposed strategy in two LLVM‐based dynamic binary translators, namely OI‐DBT and HQEMU, and show that these two translators can achieve average speedups of 1.26x and 1.15x in MiBench and Spec Cpu benchmarks, respectively.

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Empirical study of the dynamic behavior of JavaScript objects

Wei

Xhakaj

Ryder

2015

Softw. Pract. Exper.

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SUMMARYDespite the popularity of JavaScript for client-side web applications, there is a lack of effective software tools supporting JavaScript development and testing. The dynamic characteristics of JavaScript pose software engineering challenges such as program understanding and security. One important feature of JavaScript is that its objects support flexible mechanisms such as property changes at runtime and prototype-based inheritance, making it difficult to reason about object behavior. We have performed an empirical study on real JavaScript applications to understand the dynamic behavior of JavaScript objects. We present metrics to measure behavior of JavaScript objects during execution (e.g., operations associated with an object, object size, and property type changes). We also investigated the behavioral patterns of observed objects to understand the coding or user interaction practices in JavaScript software.

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Trace-based just-in-time type specialization for dynamic languages

Cited by 79 publications

References 8 publications

Dynamic interpretation for dynamic scripting languages

Dynamic interpretation for dynamic scripting languages

Smart selection of optimizations in dynamic compilers

Empirical study of the dynamic behavior of JavaScript objects

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