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Characteristics of Dynamic JVM Languages

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Characteristics of Dynamic JVM Languages Characteristics of Dynamic JVM Languages Aibek Sarimbekov Andrej Podzimek Lubomir Bulej University of Lugano Charles University in Prague University of Lugano fi[email protected] [email protected]ff.cuni.cz fi[email protected] Yudi Zheng Nathan Ricci Walter Binder University of Lugano Tufts University University of Lugano fi[email protected] [email protected] fi[email protected] Abstract However, since the JVM was originally conceived for The Java Virtual Machine (JVM) has become an execution a statically-typed language, the performance of the JVM platform targeted by many programming languages. How- and its JIT compiler with dynamically-typed languages is ever, unlike with Java, a statically-typed language, the per- often lacking, lagging behind purpose-built language-specific formance of the JVM and its Just-In-Time (JIT) compiler JIT compilers. Making the JVM perform well with various with dynamically-typed languages lags behind purpose-built statically- and dynamically-typed languages clearly requires language-specific JIT compilers. In this paper, we aim to significant effort, not only in optimizing the JVM itself, but contribute to the understanding of the workloads imposed on also, more importantly, in optimizing the bytecode-emitting the JVM by dynamic languages. We use various metrics to language compiler, instead of just relying on the original JIT characterize the dynamic behavior of a variety of programs to gain performance [8]. This in turn requires that developers written in three dynamic languages (Clojure, Python, and of both the language compilers and the JVM understand the Ruby) executing on the JVM. We identify the differences characteristics of the JVM workloads produced by various with respect to Java, and briefly discuss their implications. languages or compilation strategies. In the case of statically-typed languages such as Java and Categories and Subject Descriptors C.4 [Performance of Scala, the execution charateristics have become rather well Systems]: Performance attributes; D.2.8 [Software Engineer- understood, thanks to established metrics, benchmarks, and ing]: Metrics—Performance measures studies [9, 11, 21–23]. In contrast, the execution character- istics of various dynamically-typed JVM languages have so General Terms Languages, Measurement, Performance far not been studied very extensively. We have previously Keywords workload characterization, dynamic metrics, presented a comprehensive toolchain [20] for workload char- 2 Java, JRuby, Clojure, Jython acterization across JVM languages , which was successfully applied in studying the differences between Scala and Java 1. Introduction workloads [21, 22]. Recently, our toolchain was applied in a study of execution characteristics of JVM languages by Li The introduction of scripting support and support for dynami- et al. [16], which included Clojure, JRuby, and Jython, as cally-typed languages to the Java platform enables scripting representatives of popular dynamic JVM languages. in Java programs and simplifies the development of dynamic In this paper, we report on the result of applying our language runtimes. Consequently, developers of literally toolchain to programs written in Java and three dynamic JVM hundreds of programming languages target the Java Virtual languages—Clojure, Python, and Ruby. We adopt similar Machine (JVM) as the host for their languages—both to methodology and base programs as Li et al. in their study [16], avoid developing a new runtime from scratch, and to benefit but present complementary metrics and results, seeking to from the JVM’s maturity, ubiquity, and performance. Today, improve understanding of the execution characteristics of the programs written in popular dynamic1 languages such as selected dynamic JVM languages. Ruby, Python, or Clojure (a dialect of Lisp) can be run Hence, the original scientific contribution of this paper is on the JVM, creating an ecosystem that boosts developer a workload characterization for selected Clojure, JRuby, and productivity. Jython programs, based on dynamic metrics such as call-site 1 We use the terms scripting, dynamic, and dynamically-typed language 2 We use the term JVM language to refer to any language that targets the interchangeably. JVM as execution platform. polymorphism, object lifetimes, object and class immutabil- Benchmark Description Input ity, memory zeroing, and object hash code usage. We present binarytrees Allocate and deallocate many binary trees 16 and compare the results for functionally equivalent programs fannkuch- Repeatedly access a tiny integer-sequence 10 written using the dynamic JVM languages and Java, and redux briefly discuss the implications. fasta Generate and write random DNA sequences 150,000 Repeatedly update hashtables and k-nucleotide fasta k-nucleotide strings output meteor- 2. Experiment Design Search for solutions to a shape packing puzzle contest 2,098 2.1 Metrics Generate a Mandelbrot set and write a portable mandelbrot 1,000 bitmap To analyze the dynamic program behavior, we collected nbody Perform an N-body simulation of the Jovian planets 500,000 various dynamic metrics3 that influence performance or hint fasta regexdna Match DNA 8-mers and substitute nucleotides for at optimization opportunities for programs executing on the IUB code output JVM. In Section 3 we present details and briefly discuss revcomp Read DNA sequences and write their reverse- fasta results for the following metrics: complement output spectral- Calculate an eigenvalue using the power method 500 Call-site Polymorphism. Hints at opportunities for opti- norm mizations at polymorphic call-sites, e.g. inline caching [13] Table 1. Benchmarks from the CLBG project (implemented (based on the number of receiver types), or method inlin- in Java, Clojure, Ruby, and Python) selected for workload ing [10] (based on the number of target methods). characterization. Field, Object, and Class Immutability. Enables load elim- ination [3] (replacing repeated accessess to immutable The closest to a benchmark suite that can be used for objects with an access to a compiler-generated temporary a rough comparison of dynamic languages is the Computer stored in a register), and identifies objects and side-effect- Language Benchmarks Game (CLBG) project5, which com- free data structures amenable to parallelization. pares performance results for various benchmarks imple- Object Lifetimes. Determines garbage-collector (GC) work- mented in many different programming languages. Each load, and aids in design and evaluation of new GC algo- benchmark has a prescribed algorithm and an idiomatic im- rithms, e.g. the lifetime-aware GC [15]. plementation in each supported language. Unnecessary Zeroing. Hints at opportunities for eliminat- In general, the benchmarks cannot be considered represen- ing unnecessary zeroing of memory for newly allocated tative of real-world applications, yet significant performance objects, which comes with a performance penalty [25]. differencens between Python compilers we considered indica- tive by the developers of the Fiorano JIT compiler [8], and Identity Hash-code Usage. Hints at opportunities for reduc- benchmarks from the CLBG project have been used in the ing header size for objects that never need to store their recent study of JVM languages by Li et al. [16]. To provide identity hash code (often derived from their memory loca- complementary results for comparable workloads, we have tion upon first request [1]). decided to adopt the approach of Li et al., and based our study The metrics were collected using our workload characteri- (mostly, but not completely) on 10 CLBG benchmarks, listed zation suite [20], which relies on bytecode instrumentation, in Table 1 along with a brief description and inputs used. and provides a near-complete bytecode coverage. We were We are aware of the general threat to validity due to the thus able to collect metrics that cover both the application use of micro-benchmarks. However, we argue that in the (and the dynamic language runtime) and the Java Class Li- context of our work, the threat is significantly mitigated by brary (including any proprietary JVM vendor-specific classes) the fact that we study the work a JVM needs to perform on a standard JVM. when executing the benchmarks (rather than bare-metal performance). 2.2 Workloads Still, to avoid relying solely on micro-benchmarks, we Any attempts at characterizing or comparing dynamic JVM complemented our workload selection with 4 real-world ap- language workloads are inevitably hampered by the lack plication benchmarks, listed in Table 2. These unfortunately of an established benchmark suite such as DaCapo [4], lack the nice property of being idiomatic implementations 4 of the same task. The eclipse and jython benchmarks come SPECjvm2008 , or the Scala Benchmark suite [22]. Even 6 7 though some language-specific benchmark suites (e.g. Py- from the DaCapo suite, while opal and clojure-script are Bench) exist, they are usually very low-level and do not allow open-source projects from GitHub. for direct comparison among different languages. 5 http://benchmarksgame.alioth.debian.org/ 3 They can only be obtained by running a program with a particular input. 6 http://opalrb.org 4 http://www.spec.org/jvm2008/, 7 https://github.com/clojure/clojurescript Application Description Input binarytrees fannkuch fasta knucleotide (language) 105 clojure-script twitterbuzz A compiler for Clojure that targets JavaScript 103 (Clojure) source 1 eclipse DaCapo 10 An integrated development
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