NDLI: Mining Method Handle Graphs for Efficient Dynamic JVM Languages

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Mining Method Handle Graphs for Efficient Dynamic JVM Languages

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Mining Method Handle Graphs for Efficient Dynamic JVM Languages

Content Provider	ACM Digital Library
Author	Heidinga, Daniel Xu, Shijie Bremner, David
Abstract	The Java Virtual Machine (JVM) has been used as an execution platform by many dynamically-typed programming languages such as Ruby, Python, and Groovy. The main challenge to compile such dynamic JVM languages is choosing the most appropriate implementation of a method for various types of an object at runtime. To address this challenge, a new Java bytecode instruction, invokedynamic, has been introduced, allowing users to control the linkage between a call site and a method implementation. With this instruction, a method handle that refers to a method is linked to the call site and then potentially transforms the invocation to a real implementation. As a referenced method of a method handle might in turn refer to other method handles, multiple method handles constitute a Method Handle Graph (MHG). In order to support more efficient dynamic JVM language implementations, we present methods to mine patterns in the method handle graph. We investigate two kinds of method handle patterns: the transformation pattern and the instance pattern. The transformation pattern refers to a composition of multiple method handle transformations, and the instance pattern refers to equivalent method handles in MHGs. Both patterns are mined by the presented suffix tree and equivalency detector, respectively, which are implemented as modules in the Method Handle Mining System (MHMS). Our experiments on the JRuby Micro-Indy benchmark reveal several findings: a) the frequency of different transformation patterns varies significantly, and the JRuby interpreter prefers a small number of transformation patterns, b) a large proportion of method handles, 28.83%, are equivalent, and most of these equivalent method handles can be eliminated to reduce consumed memory, and c) the distribution of equivalent sets for length-two method handle chains is also uneven. For example, only 7% of these sets have more than 30 equivalent method handle chains. We believe these insights are important steps towards further optimizations based on method handle graphs.
Starting Page	159
Ending Page	169
Page Count	11
File Format	PDF
ISBN	9781450337120
DOI	10.1145/2807426.2807440
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-09-08
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Method handle Dynamically typed language Data mining Jsr292 Invokedynamic
Content Type	Text
Resource Type	Article

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