JVM-Hosted Languages: They Talk the Talk, but do they Walk the Walk?

介绍了五种(Java、Clojure、JRuby、Jython、Scala)在JVM中运行的语言以及它们运行中的一些特点和性能
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1. JVM-Hosted Languages: They Talk the Talk, but do they Walk the Walk? Wing Hang Li David R. White Jeremy Singer School of Computing Science, University of Glasgow w.li.2@research.gla.ac.uk, david.r.white@glasgow.ac.uk, jeremy.singer@glasgow.ac.uk Abstract libraries and reducing the risk of adopting the new language. Non- The rapid adoption of non-Java JVM languages is impressive: ma- Java languages can take advantage of automatic memory manage- jor international corporations are staking critical parts of their soft- ment and adaptive optimizations provided by the JVM. However, ware infrastructure on components built from languages such as JVM implementations expect to execute Java bytecode, therefore Scala and Clojure. However with the possible exception of Scala, the performance of non-Java bytecode may be poor. For instance, there has been little academic consideration and characterization Java uses static typing but there are JVM languages that use dy- of these languages to date. In this paper, we examine four non- namic typing instead. The JVM introduced support for dynamic Java JVM languages and use exploratory data analysis techniques types in Java 7; support for other features, such as tails calls, con- to investigate differences in their dynamic behavior compared to tinuations and interface injection, is currently in progress1 . Java. We analyse a variety of programs and levels of behavior to The adaptive optimization heuristics and garbage collection draw distinctions between the different programming languages. (GC) algorithms used by the JVM are other possible reasons for We briefly discuss the implications of our findings for improving the poor performance of non-Java JVM programming languages. the performance of JIT compilation and garbage collection on the Previous studies [26][25] have shown that the characteristic behav- JVM platform. ior of Scala is different from that of Java. They suggest that it is possible to improve Scala’s performance on a JVM based on those Categories and Subject Descriptors C.4 [Performance of Sys- differences. Our work examines the behavior of three other JVM tems]: Performance attributes; D.3.4 [Programming Languages]: programming languages; Clojure, JRuby and Jython. All three lan- Processors—Compilers guages are dynamically typed and JRuby and Jython in particular are generally used as scripting languages. General Terms Measurement, Performance Our static and dynamic analyses reveal that significant amounts of Java code are used by non-Java languages. We apply exploratory Keywords Dynamic analysis; Java virtual machine; JVM byte- data analysis methods [12] to data gathered through dynamic pro- code filing of programs written in five JVM languages. Instruction-level analysis shows that bytecode compiled from non-Java languages 1. Introduction exhibits instruction sequences never or rarely used by Java. We Java is one of the most popular programming languages currently find differences in method size and stack depths between JVM in use. Much of its success may be attributed to the Java Virtual Ma- languages but no difference in method and basic block hotness. chine (JVM). JVM implementations are available on a wide variety Heap profiling reveals that non-Java objects are generally smaller of platforms, allowing developers to “write once, run anywhere”. and shorter lived than Java objects. Non-Java languages also make In practice, this means that an application is compiled to bytecode more use of boxed primitive types than Java. We discuss possi- and this bytecode can be executed on any platform with an avail- ble changes to JVM optimization heuristics based on the observed able JVM. The JVM also provides a sandboxed environment for behavior of non-Java languages. security, and adaptive optimizations that improve the performance of applications. 1.1 Our Contribution An increasing number of programming language toolchains This empirical investigation makes two key contributions: produce bytecode that is executable on the JVM. Developers choose to use these languages for features or programming para- digms that are not available in Java. Many of these JVM languages • We explore the behavior of benchmarks written in five JVM can interoperate with Java, allowing developers to use existing Java programming languages (wider scope than earlier studies). To the best of our knowledge, the behavior of Clojure, JRuby and Jython programs has never been previously studied in a systematic fashion. • We examine the proportion of Java code used by each non-Java programming language through static analysis of each program- Copyright c ACM, 2013. This is the author’s version of the work. It is ming language’s library and dynamic analysis of a set of bench- posted here by permission of ACM for your personal use. Not for redis- tribution. The definitive version was published in PPPJ’13, September 11–13, marks. With this information, we can estimate the effectiveness http://dx.doi.org/10.1145/2500828.2500838. of custom optimizations for such JVM-hosted languages. , . Copyright c ACM [to be supplied]. . . $15.00. http://dx.doi.org/10.1145/ 1 e.g. http://openjdk.java.net/projects/mlvm/

2.2. Methodology benchmark workload int fp ptr str In this section, we describe the JVM languages in our study, the Binary-Trees tree depth 13 Y Fannkuch-Redux sequence length 9 Y benchmarks written in those languages, the tools we use to profile Fasta 150000 sequences Y them and the analysis techniques we use to identify interesting K-Nucleotide Fasta output Y Y behaviors. We will provide the source code, experimental scripts Mandelbrot 500 image size Y and raw data from our empirical study via our project repository2 . Meteor-Contest 2098 solutions Y N-body 100000 steps Y 2.1 JVM Languages Regex-DNA Fasta output Y Reverse-Complement Fasta output Y We examine five JVM languages: Spectral-Norm 160 approximations Y Java is the original JVM programming language. It is object- oriented and statically typed. We use Java program behavior Table 1: Description of the Computer Languages Benchmark Game as the baseline in all our quantitative comparisons. (CLBG) corpus of programs used in our study, indicating whether Clojure [9] is a LISP dialect, with support for advanced con- a program mostly manipulates integers, floating-point numbers, currency features, including actors and software transactional pointers or strings. memory. It is a functional language with dynamic typing. JRuby [14] is a Java-based implementation of the Ruby program- the same input. ming language. It is a dynamic, object-oriented language. The Clojure applications we profiled were: Jython [17] is an implementation of the Python language for the Noir - a web framework written in Clojure. We profile a blog site Java platform. It is a dynamic, object-oriented language. created using Noir 1.2.2 that was provided by the Noir website. Scala [15] is a functional and object-oriented programming lan- We use a Perl script to simulate user interaction with the blog guage, with a static typing discipline. It has advanced facilities site as input. for typing and concurrency. Leiningen - a build automation tool that simplifies download- ing and building Clojure software. We profile Leiningen 2.1.0 2.2 Program Suite building and packaging the Noir blog site into an executable We searched for suitable examples of applications, representative uberjar. of each JVM programming language in our study. We used 10 Incanter - an R-like statistical computing and graphing tool. Many benchmark programs from the Computer Languages Benchmark of its modules are based on Java libraries, therefore we only Game3 (CLBG) project. The site compares programming language profile the Incanter 1.5.0 core module running its associated performance by implementing a solution to a set of computing unit tests. problems in different programming languages. The benchmarks use the same underlying algorithm, therefore they may ignore program- The JRuby applications we profiled were: ming language features that would enable improved performance Ruby on Rails - a popular web framework, using JRuby to run using a different algorithm. The project organizers acknowledge on a JVM. We created a blog site using Ruby on Rails 3.2.13 that the style of each benchmark problem implementation has a sig- and the Rails website’s tutorial. A Perl script was again used to nificant effect on its relative performance. A recent empirical study interact with the blog site while it was being profiled. of the R scripting language [13] also used the CLBG benchmarks. Warbler - an application that packages a Ruby or Ruby on Rails The website includes benchmark implementations in four of the application into a Java jar or war file. We profile Warbler 1.3.6 five JVM languages (excluding Jython) outlined in Section 2.1. The as it builds and packages the Ruby on Rails blog site used available Python benchmarks were converted from Python v3 to v2 previously, into an executable war file. and minor changes were made to remove multiprocessing and fix output format problems for Jython compatibility. Table 1 outlines Lingo - an open-source Ruby application for the automatic index- the general characteristics of the 10 CLBG benchmark problems ing of scientific texts. We profile Lingo 1.8.3 as it indexes “Alice we use for comparative analysis and their workloads. Three of the in Wonderland” using its standard English dictionary settings. benchmarks use text output from the Fasta benchmark (consisting Jython is mainly used as a scripting language, therefore we of 150,000 randomly generated DNA sequences) as their input. could not find any suitable large applications written in this lan- We supplement the CLBG benchmarks with additional bench- guage. We had difficulty porting Python applications since the lat- marks that are representative examples of non-trivial applications est Python language specification and libraries are not fully imple- written in the JVM programming languages. The DaCapo Bench- mented by Jython and therefore we only profile the CLBG Python mark Suite ver. 9.12 for Java [1] and the Scala Benchmark Suite ver. (Jython) benchmarks. 0.1.0 [26] contain open source applications chosen from a range of domains. We set the workload size of these benchmark suites to 2.3 Data Generation small, to limit the size of the traces produced by the heap profiler. We gather data from the benchmarks using two profilers, as shown We could not find any Clojure, JRuby or Jython benchmark in Figure 1. The first profiler we use is a modified version of JP2 suites, and there are few individual open source, real-world applica- 2.1 [23] to collect information about the bytecode instructions and tions. We select three examples of applications written in Clojure methods used by each benchmark. The original version of JP2 and JRuby and use them as benchmarks, combined with suitable records method and basic block execution counts; our version also workloads. The examples were chosen because they were com- records the bytecode instructions within each basic block. Only 198 monly used, executable from a command line and repeatable with of the 202 bytecodes are recorded distinctly since bytecodes simi- lar to LDC, JSR and GOTO are aggregated. The data produced by JP2 2 http://www.dcs.gla.ac.uk/ ~wingli/jvm_language_study is non-deterministic. However, using 3 sets of traces, we found that 3 http://shootout.alioth.debian.org/ the figures recorded for bytecodes and methods executed display a

3. Data  Gathering   Exploratory  Data   Sewe et al. [26] use this approach to characterize Scala and Java Analysis   programs. Blackburn et al. [1] apply PCA to dynamic processor- Object  Level   level metrics to visualize Java benchmark diversity. Garbage   Object  Creation   Object   Boxplots: We use boxplots to summarize distributions of data Collection   and  Deaths   Demographics   for measurements on methods and objects. The following conven- Traces   JVM   tions are used for all boxplots in this paper. Each box denotes the Method  Level   interquartile range, with the median marked by a thick black stroke. Principal   The boxplot whiskers mark the 9th and 91st percentiles; values out- Call/Ret  Events   Components   Dynamic   Analysis   side this range are considered outliers. The absolute value reported Bytecode   to the right of each box is the maximum recorded observation for Traces   Instruction  Level   that program. The use of boxplots to summarize static and dynamic Instruction  Mix   N-­‐Grams   characteristics of Java programs is becoming common practice, e.g. Models   [7, 34]. Heat maps: We use heat maps to compare object lifetimes be-   tween JVM languages. Darker shadings indicate a higher propor- Figure 1: Schematic diagram of profiling data generation and anal- tion of objects within a lifetime range. The left of the heat map ysis techniques. represents objects with a short lifetime while the right represents longer lived objects. Dufour et al. [6] use relative frequencies to compare dynamic metrics values across Java benchmarks. standard deviation of less than 0.2%. The second profiler we use is Elephant Tracks (ET) 0.6 [21, 22], a heap profiler that provides information about object allocations 3. Results and deaths. Some benchmarks failed to complete using ET; possi- 3.1 Java vs. Non-Java Code bly due to the high overhead or instrumentation used by the profiler. Neither JP2 or ET provide complete coverage of all the classes used All non-Java JVM languages use Java classes to a certain extent. by a JVM language. JP2 instruments each method used, therefore This may be exhibited (a) statically, in terms of the proportion there is no record of classes that are only accessed by their fields. of shipped bytecode classes that are compiled from Java source ET has a known bug where objects in the constant pool have no code, and (b) dynamically, in terms of the proportion of executed allocation record. However, these problems occur infrequently. methods that are compiled from Java source code. The proportion We start by compiling the source code of each benchmark to of Java used by each non-Java programming language will have an JVM bytecode using the canonical compiler for its language. The impact on the effectiveness of any language-specific optimization exception is Jython, which uses high-level language interpretation. that we might propose. Table 5 details whether each benchmark is compiled to bytecode A static analysis of the language libraries distributed with each ahead-of-time (AOT), just-in-time (JIT) or interpreted. The JVM non-Java JVM language reveals the proportion of the library code language toolchain versions we use are Java 1.6 30, Clojure 1.3.0, that is implemented in Java. Each class in the library is classified JRuby 1.6.7, Jython 2.5.3 and Scala 2.9.2. We execute the compiled using the extension of the corresponding source file. If this source bytecode (or run the Jython interpreter) using the Java HotSpot 64- file information is not stored in the class metadata, we locate and bit Server VM 1.6.0 30, running on Linux kernel v3.2.0-48, x86 64 classify the source file manually via source code inspection. build. The traces produced by JP2 and ET are parsed to extract the Table 2 shows that the proportion of Java code within the Clo- desired metrics. jure and Scala libraries is exceeded by non-Java code. However, the proportion of Java code within the JRuby and Jython libraries exceeds the amount of non-Java code by a significant factor. This 2.4 Analysis Techniques suggests that JRuby and Jython may not benefit greatly from op- Figure 1 shows the range of exploratory data analysis techniques timizations based on differences between Java and non-Java pro- we apply to gain an understanding of the dynamic behavior of the gramming language behavior. To verify whether this is the case, various programs. We endeavor to follow appropriate past practice we separate Java and non-Java bytecodes, methods and objects by in the community in presenting our results, as indicated in the rest filtering the dynamic traces obtained from the profiling tools, using of this section. the same method we used to classify the classes in the language N-gram models: An N -gram is a sequence of N consecutive libraries. This allows us to explore the behavior of the non-Java JVM bytecode instructions within a single basic block. We consider language methods and objects in isolation. the coverage of observed N -grams in relation to the theoretical Table 5 details run-time statistics for the profile traces of all our maximum– if there are 198 observable bytecode instructions, then benchmark programs. The table shows that some benchmarks (e.g. there can be 198N N -gram sequences. Clojure and Scala Regex-Dna) never execute a high percentage of O’Donoghue et al. [16] introduce the concept of N -gram anal- non-Java bytecode due to their reliance on Java library classes. ysis for Java execution traces. They measure dynamic 2-gram fre- The data in Table 5 shows that the proportion of Java code ex- quencies for a set of Java benchmarks, to identify candidates for ecuted at run-time is similar to the results of the static analysis. bytecode super-instructions. JRuby and Jython, as expected, use a small proportion of instruc- Principal Component Analysis: (PCA) [27] is a frequently tions, methods and objects produced from JRuby or Jython source used technique for dimension reduction, to aid visualization and code. The Clojure CLBG benchmarks use less Java than the three comprehension. PCA works by choosing and combining dimen- application benchmarks, indicating that Clojure applications are sions that contain the greatest variance. For each individual bench- more likely to depend on Java components. For instance, the Noir mark program, we measure the relative frequency of each JVM blog uses Jetty, a web server written in Java, while the Clojure com- bytecode instruction to produce a 198-vector of values in range piler used by Leiningen is implemented in Java. Scala is the only [0, 1] or a 39204-vector of values for 2-grams. We apply PCA to non-Java JVM language that uses a significant proportion of in- reduce the number of dimensions from 198 or 39204 to 4. structions, methods and objects implemented in its own language.

4. Java Non-Java Language Classes Methods Instructions Classes Methods Instructions Clojure 1.3.0 696 (24%) 3887 (33%) 146333 (24%) 2171 (76%) 7813 (67%) 455567 (76%) JRuby 1.6.7 5167 (65%) 37877 (87%) 1987564 (98%) 2767 (35%) 5874 (13%) 47130 (2%) Jython 2.5.3 4002 (68%) 45813 (86%) 1592392 (96%) 1852 (32%) 7641 (14%) 63015 (4%) Scala 2.9.2 140 (3%) 1195 (1%) 25840 (3%) 5230 (97%) 100616 (99%) 866451 (97%) Table 2: The number (respectively, proportion) of classes, methods and instructions from Java and non-Java source files within non-Java programming language libraries. The results indicate that non-Java JVM languages are heavily 3.3 Method-level Results dependent on Java, therefore any efforts to tune the adaptive op- This section compares the method sizes, stack depths, method timizations or garbage collection algorithms used by a JVM must hotness and basic block hotness between the five JVM languages. take care not to degrade Java performance. The results also indi- cate that observed differences in the behavior between non-Java JVM languages and Java will only be of use to Scala and, to a 3.3.1 Method Sizes lesser extent, Clojure. Java is a necessary part of non-Java JVM Method inlining heuristics are generally parameterized on caller/ languages and therefore we also look at unfiltered data in our in- callee static method size [29], therefore we examine the method struction, method and object level results. This will reveal if the sizes commonly used by different JVM languages. The boxplots behavior of non-Java applications, including the Java code that they in Figure 3 show the distribution of static method sizes for each use, differs from standard Java applications. benchmark, measured in terms of bytecode instructions, weighted by dynamic invocation frequency. The median method size for 3.2 Instruction-level Results CLBG benchmarks written in Java varies considerably compared to In this section, we look for differences in the bytecode executed by the DaCapo benchmarks. This is not true for the non-Java JVM lan- the JVM for the 5 JVM languages. guages, whose unfiltered median method sizes show less variation. The filtered median method sizes for Clojure, JRuby and Jython 3.2.1 N-gram Coverage show much more variability. However, the results of the dynamic This investigation considers the dynamic N -gram vocabulary ex- analysis in Table 5 has shown that these methods represent a small hibited by each language. A use of N -gram g for language L means proportion of the executed methods. Scala shows the most inter- at least one of the benchmark implementations in L executes a ba- esting behavior; the median method size is small, typically three sic block containing g at least once. instructions only, for most of its benchmarks. Table 3 shows that the Java N -gram vocabulary is more diverse than for non-Java languages. However, it should be noted that 3.3.2 Stack Depths there are 20 Java benchmarks in this study, while there are 13 or fewer benchmarks for Clojure, JRuby and Jython. Scala has 20 Each thread maintains its own stack; we record the thread’s current benchmarks, but it still displays a smaller N -gram vocabulary than stack depth whenever a method entry or exit occurs in an ET Java. Table 4 shows that, despite Java using diverse N -grams, each trace. We report the distribution of recorded stack depths for each non-Java JVM language still uses N -grams not used by the Java benchmark. However, exceptional method exits are not recorded by benchmarks. Moreover, these N -grams are executed frequently; ET and method stacks can switch between threads while sleeping, for instance, 58.5% of 4-grams executed by Scala are not found in leading to a small amount of inaccuracy in our results. Figure 4 bytecode executed by Java. This indicates that a significant amount shows that the median stack depth for the CLBG benchmarks is of the N -grams used by non-Java JVM languages are, at the very less than 50 for all of the JVM languages. However, the median least, uncommonly used by Java. stack depth is higher for larger applications. In particular, Clojure and Scala applications display larger stack depths, possibly due to recursion used by these functional languages. 3.2.2 Principal Component Analysis We apply PCA to the bytecode instruction frequencies and 2-gram 3.3.3 Method and Basic Block Coverage frequencies, producing the scatter plots in Figure 2. The unfiltered We measure method and basic block hotness as per [24] by de- 1 and 2-gram PCA graphs show that benchmarks belonging to dif- termining the most frequently executed 20% of methods and basic ferent JVM languages display a low amount of divergence; forming blocks and measuring the amount of executed bytecodes that they a single cluster with few outliers. This is likely due to the amount cover. The results in Table 5 show that in most cases, the most fre- of Java used by non-Java JVM languages, resulting in similar in- quent 20% of methods will cover almost all of the executed byte- structions being used. The filtered 1 and 2-gram PCA graphs show code. One anomaly is the revcomp benchmark for Clojure, Java, JRuby and Jython benchmarks forming distinct clusters away from JRuby and Scala, for which there are relatively many frequently ex- Java, Clojure and Scala. The clustering is most apparent in the fil- ecuted methods that are small in size. However, the most frequent tered 2-gram PCA graph. 20% of basic blocks covers more than 98% of bytecodes executed The PCA graphs indicate that N -grams produced by the JRuby for all benchmarks. There is little difference in the method and ba- compiler and the Jython interpreter are distinct from other JVM sic block hotness between Java and non-Java benchmarks. languages in the frequency and type of 1 and 2-grams they use. Conversely, Java, Clojure and Scala use similar 1 and 2-grams, even when Java code has been filtered from the Clojure and Scala 3.4 Object-level Results N -grams. The PCA graphs indicate that for shorter N -grams, only In this section, we compare object lifetimes, object sizes and the the filtered JRuby and Jython show different behavior compared to amount of boxed primitives used by the five JVM languages in our Java. study.

5. 0.15 0.25 clojure clojure java java 0.1 jruby 0.2 jruby jython jython scala scala 0.05 0.15 Principal Component 2 Principal Component 2 0 0.1 −0.05 0.05 −0.1 0 −0.15 −0.05 −0.2 −0.1 −0.25 −0.15 −0.1 −0.05 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 −0.2 −0.1 0 0.1 0.2 0.3 0.4 0.5 Principal Component 1 Principal Component 1 (a) Unfiltered 1-gram (b) Filtered 1-gram 0.15 0.2 clojure clojure java java jruby 0.15 jruby 0.1 jython jython scala scala 0.1 0.05 Principal Component 2 Principal Component 2 0.05 0 0 −0.05 −0.05 −0.1 −0.1 −0.15 −0.15 −0.2 −0.2 −0.25 −0.2 −0.15 −0.1 −0.05 0 0.05 0.1 −0.1 −0.05 0 0.05 0.1 0.15 0.2 0.25 Principal Component 1 Principal Component 1 (c) Unfiltered 2-gram (d) Filtered 2-gram Figure 2: PCA scatter plots illustrating variation in bytecode instruction mix across different JVM languages.

6. xalan Java 2881 sunflow 6308 pmd 2881 lusearch 1060 luindex 826 Largest size (Bytecodes) javac 10427 h2 1269 fop 33089 batik 41033 avrora 2899 spectralnorm 767 revcomp 558 regexdna 767 nbody 767 meteor 558 mandelbrot 558 knucleotide 767 fasta 558 fannkuch 558 binarytrees 558 0 20 40 60 80 100 noir Clojure 10839 noir Clojure 10839 leiningen 11994 leiningen 11994 incanter 11994 incanter 11994 Largest size (Bytecodes) spectralnorm 10839 spectralnorm 10839 revcomp 10839 revcomp 10839 regexdna 10839 regexdna 10839 nbody 10839 nbody 10839 meteor 10839 meteor 10839 mandelbrot 10839 mandelbrot 10839 knucleotide 10839 knucleotide 10839 fasta 10839 fasta 10839 fannkuch 10839 fannkuch 10839 binarytrees 10839 binarytrees 10839 0 20 40 60 80 100 0 20 40 60 80 100 warbler JRuby 26278 warbler JRuby 550 lingo 26278 lingo 1385 jrails 23585 jrails 1047 Largest size (Bytecodes) spectralnorm 9267 spectralnorm 187 revcomp 9267 revcomp 114 regexdna 9267 regexdna 381 nbody 9267 nbody 599 meteor 9267 meteor 708 mandelbrot 9267 mandelbrot 308 knucleotide 9267 knucleotide 317 fasta 21917 fasta 577 fannkuch 9267 fannkuch 542 binarytrees 9267 binarytrees 282 0 20 40 60 80 100 0 20 40 60 80 100 Jython Jython spectralnorm 7810 spectralnorm 3586 revcomp 7810 revcomp 3941 Largest size (Bytecodes) regexdna 7810 regexdna 3941 nbody 7810 nbody 3586 mandelbrot 7810 mandelbrot 2628 knucleotide 7810 knucleotide 3586 fasta 7810 fasta 3586 fannkuch 7810 fannkuch 3586 binarytrees 7810 binarytrees 2628 0 20 40 60 80 100 0 20 40 60 80 100 tmt Scala 2899 tmt Scala 538 specs 995 specs 647 scalaxb 2881 scalaxb 1110 scalatest 1439 scalatest 1439 scalariform 1125 scalariform 1125 Largest size (Bytecodes) scalap 995 scalap 678 scaladoc 2329 scaladoc 2329 scalac 3478 scalac 3330 kiama 995 kiama 960 apparat 1824 apparat 1824 spectralnorm 995 spectralnorm 75 revcomp 995 revcomp 93 regexdna 995 regexdna 192 nbody 995 nbody 234 meteor 995 meteor 188 mandelbrot 995 mandelbrot 136 knucleotide 995 knucleotide 136 fasta 995 fasta 201 fannkuch 995 fannkuch 118 binarytrees 995 binarytrees 169 0 20 40 60 80 100 0 20 40 60 80 100 Method Size (Bytecodes) Method Size (Bytecodes) Figure 3: The distribution of method sizes for each benchmark, grouped by language. Boxplots in the right column are based on filtered (i.e. non-Java) methods only. Methods of size greater than 100 instructions are off the scale.

7. language filtered? 1-gram 2-gram 3-gram 4-gram Java No 192 5772 31864 73033 Clojure Yes 118 (0.61) 1217 (0.21) 3930 (0.12) 7813 (0.10) Clojure No 177 (0.92) 4002 (0.68) 19474 (0.58) 40165 (0.51) JRuby Yes 54 (0.28) 391 (0.07) 1212 (0.04) 2585 (0.03) JRuby No 179 (0.93) 4482(0.76) 26373 (0.79) 64399 (0.81) Jython Yes 48 (0.25) 422 (0.07) 1055 (0.03) 1964 (0.02) Jython No 178 (0.92) 3427 (0.58) 14887 (0.44) 27852 (0.35) Scala Yes 163 (0.84) 2624 (0.45) 11979 (0.36) 30164 (0.38) Scala No 187 (0.97) 3995 (0.68) 19515 (0.58) 45951 (0.58) Table 3: N-gram coverage for various JVM languages. Number in brackets shows the value relative to Java language filtered? 1-gram 2-gram 3-gram 4-gram Clojure Yes 2 193 (0.11) 1957 (0.46) 6264 (0.77) Clojure No 2 348 (0.05) 4578 (0.23) 15824 (0.43) JRuby Yes 1 44 (0.02) 399 (0.14) 1681 (0.42) JRuby No 1 512 (0.01) 7659 (0.08) 30574 (0.26) Jython Yes 1 38 (0.07) 412 (0.19) 1491 (0.56) Jython No 1 161 (0.01) 2413 (0.06) 8628 (0.19) Scala Yes 0 288 (0.03) 4168 (0.27) 18676 (0.69) Scala No 0 335 (0.02) 4863 (0.23) 21106 (0.59) Table 4: Number of N -grams used by non-Java benchmarks that are not used by Java benchmarks. For 2 ≤ N ≤ 4 the number in brackets represents the value relative to total number of filtered or unfiltered N -grams covered by these non-Java N -grams. 3.4.1 Object Lifetimes Numeric and RubyRational objects allocated. For Jython, we The heat maps in Figure 5 show the percentage of objects allocated count PyBoolean, PyComplex, PyComplexDerived, PyFloat, within 5% intervals of the total execution time. A black colored PyFloatDerived, PyInteger, PyIntegerDerived, PyLong and interval indicates that 10% or more of the total objects allocated PyLongDerived objects allocated. are within the lifetime range. We observe that most objects die The results in Table 5 shows that Java benchmarks use very lit- young for the DaCapo and Scala benchmark suites and the three tle boxing. However, non-Java benchmarks are more likely to use a Clojure application benchmarks, following the weak generational high proportion of boxed primitives. The proportion of boxed prim- hypothesis [32]. However the lifetimes of objects allocated by itives used by Clojure is unexpectedly high, even though primitives the Java, Scala and Clojure CLBG benchmarks are more varied. can be used in Clojure. Conversely, all primitives must be boxed in More objects belonging to the Scala benchmark suite die young Scala, yet the level of boxing used is lower than for Clojure. compared to the DaCapo benchmarks, confirming previous results by Sewe et. al [25]. However, more Clojure objects live longer 4. Related Work compared to Java. Unfiltered JRuby and Jython display interesting Many researchers have sought to characterize Java programs. How- behavior; their objects either live for less than 5% of the total ever to date, large-scale studies based on corpora such as Qualitas execution time or for the duration of program execution. [30] have been restricted entirely to static analysis, e.g. [3, 31]. 3.4.2 Object Sizes Dynamic studies of Java programs have generally focused on one particular aspect such as instruction mix [19] or object demo- We examine the distribution of object sizes, weighted by their graphics [5, 10]. Stephenson and Holst [28] present an analysis of dynamic allocation frequency. The size of java.lang.Object is multicodes, which are a generalization of bigrams [16] to variable 16 bytes for the JVM we use. The boxplots in Figure 6 show that length dynamic bytecode sequences. the object size for most non-Java JVM languages is dominated by Dufour et al. [6] present a broader dynamic analysis study. They only one or two sizes. This can be seen from the median object size introduce a range of new metrics to characterize various aspects in the unfiltered JRuby and Jython boxplots and the filtered Clojure of Java program behaviour including polymorphism, memory use and Scala boxplots. However, the median object size for Java varies and concurrency. The explicit design rationale for their metrics between 24 to 48 bytes. By comparing the unfiltered and filtered is to enable quantitative characterization of a Java program with boxplots, we see that Clojure and Scala use smaller objects more relevance to compiler and runtime developers. Sarimbekov et al. frequently than Java. [24] motivate the need for workload characterization across JVM languages. They propose a suite of dynamic metrics and develop 3.5 Boxed Primitives a toolchain to collect these metrics. Our work relies in part on The final metric in our study measures boxing, in terms of the pro- earlier incarnations of their tools (e.g. JP2 [23]) and has the same portion of allocated objects that are used to wrap primitive types. motivation of JVM-based cross-language comparison. For Java, Clojure and Scala, we count the number of Java Boolean, Radhakrishnan et al. [20] study the instruction-level and method- Byte, Character, Double, Float, Integer, Long and Short ob- level dynamic properties of a limited set of Java benchmarks in or- jects allocated, as well as BigDecimal and BigInteger. JRuby der to make proposals about architectural performance implications and Jython use Java boxed classes and implement their own box- of JVM execution. They discuss processor cache configuration ing classes. For JRuby, we count RubyBoolean, RubyComplex, and instruction-level parallelism. Blackburn et al. [1] use various RubyBigDecimal, RubyBigNum, RubyFloat, RubyFixNum, Ruby- dynamic analysis techniques including PCA on architecture-level

8. Java >=10 binarytrees fannkuchredux 9 fasta knucleotide 8 mandelbrot % of Object Deaths Benchmark Name meteor 7 nbody regexdna 6 revcomp 5 spectralnorm avrora 4 h2 jython 3 luindex lusearch 2 pmd sunflow 1 xalan 0 0 20 40 60 80 100 % of Maximum Object Lifetime Clojure (unfiltered) Clojure (filtered) >=10 >=10 binarytrees binarytrees fannkuchredux 9 fannkuchredux 9 fasta 8 fasta 8 knucleotide knucleotide % of Object Deaths % of Object Deaths 7 7 mandelbrot mandelbrot Benchmark Name Benchmark Name meteor 6 meteor 6 nbody 5 nbody 5 regexdna regexdna 4 4 revcomp revcomp 3 3 spectralnorm spectralnorm incanter 2 incanter 2 lein lein 1 1 noir noir 0 0 0 20 40 60 80 100 0 20 40 60 80 100 % of Maximum Object Lifetime % of Maximum Object Lifetime JRuby (unfiltered) >=10 binarytrees 9 fannkuchredux 8 % of Object Deaths fasta 7 Benchmark Name 6 knucleotide 5 JRuby filtered lifetimes are not shown. nbody 4 Filtered objects account for less than 1% regexdna of total objects for all JRuby benchmarks 3 revcomp 2 1 spectralnorm 0 0 20 40 60 80 100 % of Maximum Object Lifetime Jython (unfiltered) >=10 binarytrees 9 fannkuchredux 8 fasta % of Object Deaths 7 Benchmark Name knucleotide 6 mandelbrot 5 Jython filtered lifetimes are not shown. nbody 4 Filtered objects account for less than 1% of total objects for all Jython benchmarks regexdna 3 2 revcomp 1 spectralnorm 0 0 20 40 60 80 100 % of Maximum Object Lifetime Scala (unfiltered) Scala (filtered) binarytrees >=10 binarytrees >=10 fannkuchredux fannkuchredux 9 9 fasta fasta knucleotide knucleotide 8 8 mandelbrot mandelbrot meteor meteor % of Object Deaths % of Object Deaths 7 7 nbody nbody Benchmark Name Benchmark Name regexdna regexdna revcomp 6 revcomp 6 spectralnorm spectralnorm apparat 5 apparat 5 kiama kiama 4 4 scalac scalac scaladoc scaladoc scalap 3 scalap 3 scalariform scalariform scalatest 2 scalatest 2 scalaxb scalaxb specs 1 specs 1 tmt tmt 0 0 0 20 40 60 80 100 0 20 40 60 80 100 % of Maximum Object Lifetime % of Maximum Object Lifetime Figure 5: Heat maps illustrating distribution of relative object lifetimes for each benchmark, grouped by language. Shorter lifetimes are on the left, longer lifetimes on the right of each graph. Darker regions denote higher proportions of objects with this lifetime. Heat maps in the right column are based on filtered (i.e. non-Java) objects only. Heat maps for filtered JRuby and Jython data are not shown due to the small proportion of allocated objects they represent.

9. xalan Java 65552 sunflow 234096 pmd 146624 lusearch 131088 luindex 131088 Largest size (Bytes) h2 524304 avrora 47336 spectralnorm 16400 revcomp 16777232 regexdna 4194448 nbody 16400 meteor 16400 mandelbrot 16400 knucleotide 2097168 fasta 16400 fannkuch 16400 binarytrees 16400 0 8 16 24 32 40 48 56 64 72 80 88 96 noir Clojure 337752 noir Clojure 2416 lein 80536 lein 48 incanter 80536 incanter 40 spectralnorm 337752 spectralnorm 40 Largest size (Bytes) revcomp 1343504 revcomp 40 regexdna 4804624 regexdna 40 nbody 337752 nbody 72 meteor 337752 meteor 40 mandelbrot 337752 mandelbrot 40 knucleotide 2555920 knucleotide 40 fasta 337752 fasta 40 fannkuch 337752 fannkuch 40 binarytrees 337752 binarytrees 40 0 8 16 24 32 40 48 56 64 72 80 88 96 0 8 16 24 32 40 48 56 64 72 80 88 96 JRuby spectralnorm 44392 revcomp 1092256 Largest size (Bytes) regexdna 2004016 nbody 44392 JRuby filtered object sizes are not shown. Filtered objects account for less than 1% knucleotide 1092256 of total objects for all JRuby benchmarks fasta 44392 fannkuch 44392 binarytrees 44392 0 8 16 24 32 40 48 56 64 72 80 88 96 Jython spectralnorm 262160 revcomp 2031632 regexdna 7801008 Largest size (Bytes) nbody 262160 Jython filtered object sizes are not shown. mandelbrot 262160 Filtered objects account for less than 1% knucleotide 2031632 of total objects for all Jython benchmarks fasta 262160 fannkuch 262160 binarytrees 262160 0 8 16 24 32 40 48 56 64 72 80 88 96 tmt Scala 131088 tmt Scala 131088 specs 109416 specs 8216 scalaxb 589840 scalaxb 128 scalatest 73936 scalatest 152 scalariform 116080 scalariform 14424 scalap 97456 scalap 8216 Largest size (Bytes) scaladoc 1048592 scaladoc 131088 scalac 262160 scalac 131088 kiama 73936 kiama 8216 apparat 267920 apparat 9456 spectralnorm 16400 spectralnorm 8216 revcomp 1048592 revcomp 8216 regexdna 4718608 regexdna 8216 nbody 16400 nbody 8216 meteor 80536 meteor 8216 mandelbrot 32784 mandelbrot 8216 knucleotide 67108880 knucleotide 1048592 fasta 16400 fasta 8216 fannkuch 16400 fannkuch 8216 binarytrees 32784 binarytrees 8216 0 8 16 24 32 40 48 56 64 72 80 88 96 0 8 16 24 32 40 48 56 64 72 80 88 96 Object Size (Bytes) Object Size (Bytes) Figure 6: The distribution of object sizes for each benchmark, grouped by language. Boxplots in the right column are based on filtered (i.e. non-Java) objects only. Boxplots for filtered JRuby and Jython data are not shown due to the small proportion of allocated objects they represent.

10. metrics to demonstrate quantitative differences between two Java benchmark suites. Their work follows on from earlier PCA-based workload characterization studies for Java: Chow et al. [2] con- xalan Java 81 sider architecture-level metrics, whereas Yoo et al. [33] consider sunflow 55 pmd 185 OS-level metrics. In contrast, we restrict attention to VM-level lusearch 50 luindex 48 metrics in our PCA characterization. h2 823 Sewe et al. [25, 26] use a range of static and dynamic analy- Greatest depth avrora 58 spectralnorm 35 revcomp 30 ses to characterize the Java DaCapo benchmark suite in relation to regexdna 37 nbody 37 their own Scala benchmark suite. Their study does not compare the meteor 30 mandelbrot 30 performance of the two languages, they state “Such a study would knucleotide 37 fasta 30 require two sets of equivalent yet idiomatic applications written in fannkuch binarytrees 30 36 both languages.” Instead, their intent was to study the character- istics of a number of real-world Java and Scala applications. To 0 50 100 150 200 the best of our knowledge, there are no similar academic studies noir Clojure 197 of other JVM languages like Clojure, JRuby and Jython. We are lein 273 aware of the intrinsic difficulties in performing objective compara- incanter 145 spectralnorm 72 tive studies of programming languages [18]. However in our study revcomp 72 we have attempted to present data in a fair and objective way: some- Greatest depth regexdna 72 nbody 72 times this means we have presented more data (e.g. filtered and meteor 105 unfiltered) to avoid bias. mandelbrot 72 knucleotide 72 Unlike the JVM platform, the .NET common language runtime fasta 72 (CLR) was originally designed to be an appropriate target archi- fannkuch 72 binarytrees 72 tecture for multiple high-level programming languages. Thus it has a slightly richer bytecode instruction set. Gough [8] evaluates the 0 50 100 150 200 merits of CLR for various high-level languages. Dick et al. [4] JRuby present a simple analysis of the dynamic behavior of .NET byte- spectralnorm 78 code for a small-scale benchmark suite. Based on their limited anal- revcomp 55 ysis, they discuss the similarities of .NET and JVM bytecode. regexdna 39 Knuth is the pioneer of dynamic characterization of programs. Greatest depth nbody 45 His empirical study of Fortran [11] was restricted by the available knucleotide 68 programs. His code acquisition techniques include ‘recovering’ fasta 66 punch cards from waste bins and ‘borrowing’ user’s programs to fannkuch 47 duplicate them while they waited in the compilation queue. While binarytrees 107 we acknowledge the limited size of our program corpus, we are confident (like Knuth) that larger corpora will be easier to collect 0 50 100 150 200 and curate in the future. Jython spectralnorm 116 revcomp 197 5. Conclusions regexdna 211 5.1 Summary Greatest depth nbody 118 mandelbrot 97 The JVM platform now plays host to a variety of programming knucleotide 116 languages, but its optimizations are implicitly tuned to the char- fasta 118 acteristics of Java programs. This paper investigates the behavior fannkuch 106 of a sample of programs written in Java, Clojure, JRuby, Jython binarytrees 103 and Scala, to determine whether Java and non-Java JVM languages behave differently. We perform static analysis on the language li- 0 50 100 150 200 braries and dynamic analysis on a set of 75 benchmarks written in tmt Scala 88 those languages. Exploratory data analysis techniques were used specs 278 scalaxb scalatest 157 94 to visually identify interesting behaviors in the data. Static and dy- scalariform scalap 508 422 namic analysis shows that non-Java JVM languages rely heavily on scaladoc 230 Java code (a) for implementing significant parts of their language Greatest depth scalac 299 kiama 380 apparat 877 runtimes and libraries, and (b) as clients of the Java standard library spectralnorm 36 revcomp regexdna 34 59 classes. nbody meteor 40 51 At the instruction-level, non-Java benchmarks produce N - mandelbrot 50 knucleotide 67 grams not found within the Java benchmarks, suggesting they do fasta 34 fannkuch binarytrees 41 59 not share precisely the same instruction-level vocabulary as Java. Java method sizes are more varied than non-Java method sizes; 0 50 100 150 200 Scala in particular has much smaller methods than the other JVM Stack depth languages. Clojure and Scala applications exhibit deeper stack depths than other JVM language benchmarks. However there is Figure 4: The distribution of method stack depths for each bench- no difference in method and basic block hotness between JVM mark, grouped by language. languages. The object lifetimes of non-Java JVM languages are generally shorter than for Java. Filtered Clojure and Scala object sizes are smaller than they are for Java. Finally, we observe that non-Java languages use more boxed primitive types than Java.

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12.language/benchmark compiled bytecodes non-Java methods non-Java method basic block objects non-Java boxed or executed bytecodes executed methods hotness hotness allocated objects primitive interpreted % % % % % use % Java/binarytrees AOT 170105156 0.00 25176723 0.00 99.81 99.99 2651267 0.00 0.00 Java/fannkuchredux AOT 141676329 0.00 1003221 0.00 99.96 99.99 2293 0.00 0.26 Java/fasta AOT 100060247 0.00 2456162 0.00 76.76 99.99 2343 0.00 0.26 Java/knucleotide AOT 1202353259 0.00 49678282 0.00 50.24 99.99 572950 0.00 0.00 Java/mandelbrot AOT 262768004 0.00 78893 0.00 99.97 100.00 2878 0.00 0.21 Java/meteor AOT 330191630 0.00 5431167 0.00 99.48 99.99 262824 0.00 0.00 Java/nbody AOT 171061794 0.00 152240 0.00 99.47 99.98 4698 0.00 0.17 Java/regexdna AOT 3947367657 0.00 226454549 0.00 99.68 100.00 312589 0.00 0.09 Java/revcomp AOT 54864024 0.00 1548046 0.00 6.69 99.97 2868 0.00 9.24 Java/spectralnorm AOT 38870722 0.00 1079088 0.00 99.76 99.94 4785 0.00 0.13 Java/avrora AOT 4043803001 0.00 322511916 0.00 99.32 99.96 991319 0.00 0.12 Java/batik AOT 812845536 0.00 40670592 0.00 74.52 99.75 ET failed to complete trace Java/fop AOT 149718163 0.00 9541593 0.00 90.68 98.88 ET failed to complete trace Java/h2 AOT 9653613964 0.00 769997845 0.00 93.64 99.98 25548956 0.00 1.92 Java/javac/sunflow AOT 16635753858 0.00 1077651364 0.00 88.01 99.50 ET failed to complete trace Java/luindex AOT 123562365 0.00 4524912 0.00 92.91 99.86 112724 0.00 0.01 Java/lusearch AOT 1140089272 0.00 50417227 0.00 99.17 99.96 1272318 0.00 0.00 Java/pmd AOT 54097593 0.00 2366460 0.00 91.24 99.15 133466 0.00 0.26 Java/sunflow AOT 1987247487 0.00 64365743 0.00 99.09 99.98 2422198 0.00 0.03 Java/xalan AOT 887655171 0.00 48845720 0.00 97.35 99.41 1117739 0.00 0.09 Clojure/binarytrees AOT 458013959 50.18 46066179 23.11 99.81 99.93 5519929 48.01 47.82 Clojure/fannkuchredux AOT 949916910 32.01 77452181 1.68 98.21 99.96 258837 0.67 0.79 Clojure/fasta AOT 430594595 40.53 37624799 9.69 90.69 99.93 2641156 45.50 45.64 Clojure/knucleotide AOT 1741954505 23.02 131910496 4.43 80.60 99.97 7165252 4.66 85.27 Clojure/mandelbrot AOT 697889749 62.96 42874651 15.85 99.82 99.94 821435 0.48 70.20 Clojure/meteor AOT 4097931808 28.21 389333271 3.91 99.02 99.98 10199180 9.40 25.69 Clojure/nbody AOT 292196152 66.32 17028481 39.02 99.18 99.90 1239830 0.13 80.85 Clojure/regexdna AOT 4689013910 0.54 226505597 0.02 99.43 99.99 507349 0.43 0.40 Clojure/revcomp AOT 224945319 44.47 18146640 0.55 55.92 99.86 233852 0.57 1.29 Clojure/spectralnorm AOT 152301546 38.49 7716311 0.80 99.38 99.78 259541 3.25 3.23 Clojure/incanter JIT 273986921 4.79 17850648 4.48 96.72 99.03 1635693 1.78 1.33 Clojure/leiningen AOT 1006923598 2.18 57874646 2.32 97.64 99.49 1278616 1.94 1.68 Clojure/noir/blog AOT 1323734731 1.37 21635558 5.04 99.81 99.90 1909561 1.09 1.11 JRuby/binarytrees AOT 3172344964 12.57 354573991 6.27 99.98 99.99 12667277 0.01 0.00 JRuby/fannkuchredux AOT 4684146884 13.34 537414857 3.14 89.65 100.00 3272573 0.04 0.01 JRuby/fasta AOT 5564934530 6.21 617823701 2.41 99.97 99.99 14888989 0.01 32.24 JRuby/knucleotide AOT 11381452118 3.63 1081798483 2.46 99.15 100.00 18740991 0.01 0.00 JRuby/mandelbrot AOT 13483710728 8.53 1654536903 0.80 99.97 99.99 ET failed to complete trace JRuby/meteor AOT 25990259655 6.26 2455403535 2.72 99.99 100.00 ET failed to complete trace JRuby/nbody AOT 7054115048 8.78 950461750 0.81 99.97 100.00 30638260 0.00 94.66 JRuby/regexdna AOT 10090042993 0.00 628235221 0.00 99.87 100.00 336937 0.41 0.10 JRuby/revcomp AOT 277284831 1.52 18636289 1.48 76.62 99.92 333311 0.41 0.11 JRuby/spectralnorm AOT 2183551569 9.14 272266633 3.32 99.96 99.99 8515852 0.02 96.21 JRuby/jrails AOT 5197249613 0.47 313574609 1.08 98.24 99.77 ET failed to complete trace JRuby/lingo JIT 12322206926 2.36 1110460420 1.73 98.88 99.88 ET failed to complete trace JRuby/warbler JIT 7943627729 1.33 526990999 1.02 99.22 99.84 ET failed to complete trace Jython/binarytrees int. 6116792936 7.77 481948160 2.21 99.84 99.99 27183665 0.04 19.42 Jython/fannkuchredux int. 4429512501 4.99 452006371 0.00 94.81 99.98 20308641 0.01 1.72 Jython/fasta int. 7361689841 6.92 758276792 0.98 99.21 99.98 11091087 0.19 44.00 Jython/knucleotide int. 10899263124 3.79 1142232229 0.00 95.75 99.99 40776401 0.03 32.48 Jython/mandelbrot int. 6260601597 5.58 645681679 0.01 94.31 99.99 20477645 0.01 95.81 Jython/nbody int. 6656716672 7.91 899402775 0.00 91.93 99.99 33011071 0.00 82.28 Jython/regexdna int. 7144141615 0.03 432717385 0.02 86.18 99.97 136423507 0.02 0.19 Jython/revcomp int. 1164263231 0.20 78797100 0.04 92.18 99.81 3090414 0.05 3.64 Jython/spectralnorm int. 2061855998 5.75 225838375 1.37 96.72 99.96 16063707 0.05 57.64 Scala/binarytrees AOT 116374550 98.60 8209028 98.56 99.67 99.92 2666944 99.37 0.40 Scala/fannkuchredux AOT 292713859 99.59 27164782 99.67 99.96 99.99 6612 5.05 15.62 Scala/fasta AOT 247153292 81.63 29521359 66.34 79.92 99.99 3621 4.97 14.50 Scala/knucleotide AOT 2743126868 92.52 279800678 91.54 96.91 99.99 4329096 32.82 65.83 Scala/mandelbrot AOT 281828727 99.48 12620894 99.27 99.93 99.98 19299 57.46 5.72 Scala/meteor AOT 38201494343 97.60 4688797729 95.62 99.99 100.00 2340559 98.48 0.88 Scala/nbody AOT 307725221 99.67 41360373 99.86 99.60 99.99 5920 2.57 6.05 Scala/regexdna AOT 3681464412 14.54 246469635 36.78 99.93 100.00 6555923 26.30 0.01 Scala/revcomp AOT 69607945 98.85 3079180 98.42 14.09 99.97 3181 5.85 7.17 Scala/spectralnorm AOT 44336176 97.61 1110874 94.41 99.75 99.92 6546 9.21 5.44 Scala/apparat AOT 5473129320 69.78 782465549 59.90 99.38 99.89 8828083 69.63 0.45 Scala/kiama AOT 96747090 44.44 8744873 72.05 95.47 99.41 534871 60.45 1.64 Scala/scalac AOT 841758028 50.77 86270143 77.48 97.11 99.82 4384353 59.82 1.09 Scala/scaladoc AOT 1009919955 56.19 111072697 76.91 97.16 99.82 4283738 62.97 0.71 Scala/scalap AOT 50006991 23.40 3387070 53.31 94.23 99.45 166594 43.44 1.52 Scala/scalariform AOT 665331715 76.76 102056949 88.80 94.47 99.25 5121304 82.18 4.53 Scala/scalatest AOT 867200843 30.23 61964606 67.16 99.90 99.96 3503330 46.58 0.13 Scala/scalaxb AOT 371552561 47.38 35445090 72.17 94.64 99.54 1432529 52.82 21.09 Scala/specs AOT 721279181 20.13 49903727 55.97 99.76 99.90 3872916 41.33 0.90 Scala/tmt AOT 44096213851 79.66 4757318965 80.30 97.88 100.00 110104114 9.14 74.64 Table 5: Summary of dynamic metrics obtained, each benchmark occupies a single row in the table.