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https://github.com/smarr/are-we-fast-yet

Are We Fast Yet? Comparing Language Implementations with Objects, Closures, and Arrays
https://github.com/smarr/are-we-fast-yet

benchmark benchmarking comparison dynamic-languages language language-implementations performance

Last synced: 15 days ago
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Are We Fast Yet? Comparing Language Implementations with Objects, Closures, and Arrays

Host: GitHub
URL: https://github.com/smarr/are-we-fast-yet
Owner: smarr
License: other
Created: 2015-12-05T16:27:05.000Z (almost 9 years ago)
Default Branch: master
Last Pushed: 2024-02-17T11:53:14.000Z (9 months ago)
Last Synced: 2024-08-01T15:07:43.774Z (3 months ago)
Topics: benchmark, benchmarking, comparison, dynamic-languages, language, language-implementations, performance
Language: Java
Homepage:
Size: 3.78 MB
Stars: 321
Watchers: 17
Forks: 36
Open Issues: 16
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE.md

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awesome-graal - Are We Fast Yet? Comparing Language Implementations with Objects, Closures, and Arrays - by @smarr (Uncategorized / Uncategorized)

README

Are We Fast Yet? Comparing Language Implementations with Objects, Closures, Arrays, and Strings
===================================================================================================

[![Build Status](https://travis-ci.org/smarr/are-we-fast-yet.svg?branch=master)](https://travis-ci.org/smarr/are-we-fast-yet)

## Goal

The goal of this project is to assess whether a language implementation is
highly optimizing and thus is able to remove the overhead of programming
abstractions and frameworks. We are interested in comparing language
implementations with each other and optimize their compilers as well as the
run-time representation of objects, closures, arrays, and strings.

This is in contrast to other projects such as the [Computer Language Benchmark
game][CLBG], which encourage finding the
smartest possible way to express a problem in a language to achieve best
performance.

##### Approach

To allow us to compare the degree of optimization done by the implementations
as well as the absolute performance achieved, we set the following basic rules:

1. The benchmark is 'identical' for all languages.
This is achieved by relying only on a widely available and commonly used
subset of language features and data types.

2. The benchmarks should use language 'idiomatically'.
This means, they should be realized as much as possible with idiomatic
code in each language, while relying only on the core set of abstractions.

For the detailed set of rules see [the guidelines](docs/guidelines.md) document.
For a description of the set of common language abstractions see [the *core*
language](docs/core-language.md) document.

The initial publication describing the project is [Cross-Language Compiler
Benchmarking: Are We Fast Yet?][3] and can be cited as follows:

> Stefan Marr, Benoit Daloze, Hanspeter Mössenböck. 2016.
> [Cross-Language Compiler Benchmarking: Are We Fast Yet?][4]
> In Proceedings of the 12th Symposium on Dynamic Languages (DLS '16). ACM.

##### Disclaimer: This is an Academic Project to Facilitate Research on Languages

To facilitate research, the goal of this project is specifically to assess the
effectiveness of compiler and runtime optimizations for a common set of
abstractions between languages. As such, many other relevant aspects such as
GC, standard libraries, and language-specific abstractions are not included
here. However, by focusing on one aspect, we know exactly what is compared.

## Current Status

Currently, we have 14 benchmarks ported to seven different languages, including
[Crystal], Java, JavaScript, Python, Ruby, [SOM Smalltalk][SOM], and [SOMns][1] (a
[Newspeak implementation][2]).

The graph below shows some older results for
different implementations after warmup, to ensure peak performance is reported:

![Peak Performance of Java, Node.js, JRuby, JRuby+Truffle, MRI, and SOMns,
last update 2016-06-20](docs/figures/all-langs-overview-1.png?raw=true)

A detailed overview of the results is in [docs/performance.md](docs/performance.md).

For a performance comparison over time, see the [timeline view on awfy-speed.stefan-marr.de](http://awfy-speed.stefan-marr.de/timeline/).
The runs are managed at [smarr/awfy-runs](https://github.com/smarr/awfy-runs).

The benchmarks are listed below. A detailed analysis including metrics for the
benchmarks is in [docs/metrics.md](docs/metrics.md).

#### Macro Benchmarks

- [CD] is a simulation of an airplane collision detector. Based on
WebKit's JavaScript [CDjs]. Originally, CD was designed to evaluate
real-time JVMs.

- [DeltaBlue] is a classic VM benchmark used to tune, e.g.,
Smalltalk, Java, and JavaScript VMs. It implements a constraint
solver.

- [Havlak] implements a loop recognition algorithm. It has been used
to compare C++, Java, Go, and Scala performance.

- [Json] is a JSON string parsing benchmark derived from the
`minimal-json` Java library.

- [Richards] is a classic benchmark simulating an operating system
kernel. The used code is based on [Wolczko's Smalltalk
version][DeltaBlue].

#### Micro Benchmarks

Micro benchmarks are based on [SOM Smalltalk][SOM] benchmarks unless noted otherwise.

- Bounce simulates a ball bouncing within a box.

- List recursively creates and traverses lists.

- Mandelbrot calculates the classic fractal. It is derived from the
[Computer Languages Benchmark Game][CLBG].

- NBody simulates the movement of planets in the solar system. It is
derived from the [Computer Languages Benchmark Game][CLBG].

- Permute generates permutations of an array.

- Queens solves the eight queens problem.

- Sieve finds prime numbers based on the sieve of Eratosthenes.

- Storage creates and verifies a tree of arrays to stress the garbage
collector.

- Towers solves the Towers of Hanoi game.

## Contributing

Considering the large number of languages out there, we are open to
contributions of benchmark ports to new languages. We would also be interested
in new benchmarks that are in the range of 300 to 1000 lines of code.

When porting to a new language, please carefully consider [the
guidelines](docs/guidelines.md) and description of [the *core*
language](docs/core-language.md) to ensure that we can compare results.

A list of languages we would definitely be interested in is on the [issues
tracker](https://github.com/smarr/are-we-fast-yet/issues?q=is%3Aissue+is%3Aopen+label%3A%22contribution+request%22).

This includes languages like Dart, Scala, Python, and Go. Other interesting
ports could be for Racket, Clojure, or CLOS, but might require more carefully
thought-out rules for porting. Similarly, ports to C++ or Rust need additional
care to account for the absence of a garbage collector.

## Getting the Code and Running Benchmarks

### Quick Start

To obtain the code, benchmarks, and documentation, checkout the git repository:

```bash
git clone --depth 1 https://github.com/smarr/are-we-fast-yet.git
```

Note that the repository relies on git submodules, which won't be loaded at that
point. They are only needed to run the full range of language implementations
and experiments.

#### Run Benchmarks for a Specific Language

The benchmarks are sorted by language in the [`benchmarks`](benchmarks) folder.
Each language has its own harness. For JavaScript and Ruby, the benchmarks are
executed like this:

```bash
cd benchmarks/JavaScript
node harness.js Richards 5 10
cd ../Ruby
ruby harness.rb Queens 5 20
```

The harness takes three parameters: benchmark name, number of iterations, and
problem size. The benchmark name corresponds to a class or file of a benchmark.
The *number of iterations* defines how often a benchmark should be executed. The
problem size can be used to influence how long a benchmark takes. Note that some
benchmarks rely on magic numbers to verify their results. Those might not be
included for all possible problem sizes.

The [rebench.conf](rebench.conf#L31) file specifies the supported problem sizes
for each benchmark.

### Using the Full Benchmark Setup

The setup and building of benchmarks and VMs is automated via
`implementations/setup.sh`. Benchmark are configured and executed with the
[ReBench](https://github.com/smarr/ReBench) tool.

To execute the benchmarks on all supported VMs, the following implementations
are expected to be already available on the benchmark machine:

- [Crystal](http://crystal-lang.org/docs/installation/index.html)
- [Node.js](https://nodejs.org/en/download/)
- [Ruby](https://www.ruby-lang.org/en/documentation/installation/)
- GraalVM, expected to be available in `implementations/graalvm`.
Please see [implementations/graalvm/README.md](implementations/graalvm/README.md)
for details.

This repository uses git submodules for some languages implementations. To
build these, additional tools are required. These include Ant, Make, Python,
and a C/C++ compiler.

The `implementations` folder contains wrapper scripts such as `mri-23.sh`,
`java8.sh`, and `node.sh` to execute all language implementations in a common
way by ReBench.

ReBench can be installed via the Python package manager pip:

```
pip install ReBench
```

The benchmarks can be executed with the following command in the root folder:

```
rebench -d --without-nice rebench.conf all
```

The `-d` gives more output during execution, and `--without-nice` means that
the `nice` tool enforcing high process priority is not used. We don't use it
here to avoid requiring root rights.

*Note:* The [rebench.conf](rebench.conf) file specifies how and which
benchmarks to execute. It also defines the arguments to be passed to the
benchmarks.

## Academic Work using this benchmark suite

- [Collecting Cyclic Garbage across Foreign Function Interfaces: Who Takes the Last Piece of Cake?][19]
T. Yamazaki, T. Nakamaru, R. Shioya, T. Ugawa, S. Chiba.
Proceedings of the ACM on Programming Languages, PLDI 2023.

- [Simple Object Machine Implementation in a Functional Programming Language][20]
Filip Říha. Bachelor's Thesis, CTU Prague, 2023.

- [Supporting multi-scope and multi-level compilation in a
meta-tracing just-in-time compiler][23]
Y. Izawa. PhD Dissertation. Tokyo Institute of Technology, 2023.

- [Optimizing the Order of Bytecode Handlers in Interpreters using a Genetic Algorithm][25]
W. Huang, S. Marr, T. Ugawa.
The 38th ACM/SIGAPP Symposium on Applied Computing, SAC 2023.

- [Who You Gonna Call: Analyzing the Run-time Call-Site Behavior of Ruby Applications][21]
S. Kaleba, O. Larose, R. Jones, S. Marr.
Proceedings of the 18th Symposium on Dynamic Languages, DLS 2022.

- [Generating Virtual Machine Code of JavaScript Engine for Embedded Systems][18]
Y. Hirasawa, H. Iwasaki, T. Ugawa, H. Onozawa. Journal of Information Processing. 2022.

- [Profile Guided Offline Optimization of Hidden Class Graphs for JavaScript VMs in Embedded Systems][24]
T. Ugawa, S. Marr, R. Jones.
Proceedings of the 14th ACM SIGPLAN International Workshop on Virtual Machines and Intermediate Languages, VMIL 2022.

- [Implementation Strategies for Mutable Value Semantics][27]
D. Racordon, D. Shabalin, D. Zheng, D. Abrahams, B. Saeta.
Journal of Object Technology, 2022.

- [Fusuma: Double-Ended Threaded Compaction][22]
H. Onozawa, T. Ugawa, H. Iwasaki.
Proceedings of the 2021 ACM SIGPLAN International Symposium on Memory Management, ISMM 2021.

- [A Surprisingly Simple Lua Compiler – Extended Version][17]
H. M. Gualandi, R. Ierusalimschy. Journal of Computer Languages. 2021.

- [Contextual Dispatch for Function Specialization][16]
O. Flückiger, G. Chari, M. Yee, J. Ječmen, J. Hain, J. Vitek. Proceedings of the ACM on Programming Languages, OOPSLA 2020.

- [GraalSqueak: Toward a Smalltalk-based Tooling Platform for Polyglot Programming][15]
F. Niephaus, T. Felgentreff, R. Hirschfeld. Proceedings of 16th International Conference on Managed Programming Languages & Runtimes, MPLR 2019.

- [Scopes and Frames Improve Meta-Interpreter Specialization][26]
V. Vergu, A. Tolmach, E. Visser. 33rd European Conference on Object-Oriented Programming, ECOOP 2019.

- [Self-Contained Development Environments][14]
G. Chari, J. Pimás, J. Vitek, O. Flückiger. Proceedings of the 14th ACM SIGPLAN International Symposium on Dynamic Languages. DLS 2018.

- [Interflow: Interprocedural Flow-Sensitive TypeInference and Method Duplication][13]
D. Shabalin, M. Odersky. Proceedings of the 9th ACM SIGPLAN International Symposium on Scala 2018.

- [Specializing a Meta-Interpreter: JIT Compilation of DynSem Specifications on the Graal VM][12]
V. Vergu, E. Visser. Proceedings of the 15th International Conference on Managed Languages and Runtimes, ManLang 2018.

- [Newspeak and Truffle: A Platform for Grace?][11]
S. Marr, R. Roberts, J. Noble, Grace'18, p. 3, 2018. Presentation.

- [Parallelization of Dynamic Languages: Synchronizing Built-in Collections][10]
B. Daloze, A. Tal, S. Marr, H. Mössenböck, E. Petrank
Proceedings of the ACM on Programming Languages, OOPSLA 2018

- [Efficient and Deterministic Record & Replay for Actor Languages][9]
D. Aumayr, S. Marr, C. Béra, E. Gonzalez Boix, H. Mössenböck
Proceedings of the 15th International Conference on Managed Languages and Runtimes, ManLang 2018.

- [Fully Reflective Execution Environments: Virtual Machines for More Flexible Software][8]
G. Chari, D. Garbervetsky, S. Marr, S. Ducasse
IEEE Transactions on Software Engineering, IEEE TSE, p. 1–20, 2018.

- [Garbage Collection and Efficiency in Dynamic Metacircular Runtimes][7]
J. Pimás, J. Burroni, J., B. Arnaud, S. Marr
Proceedings of the 13th ACM SIGPLAN International Symposium on Dynamic Languages, DLS 2017.

- [Applying Optimizations for Dynamically-typed Languages to Java][6]
M. Grimmer, S. Marr, M. Kahlhofer, C. Wimmer, T. Würthinger, H. Mössenböck
Proceedings of the 14th International Conference on Managed Languages and Runtimes, ManLang 2017.

- [Efficient and Thread-Safe Objects for Dynamically-Typed Languages][5]
B. Daloze, S. Marr, D. Bonetta, Hanspeter Mössenböck
In Proceedings of the 2016 ACM SIGPLAN International Conference on Object-Oriented Programming, Systems, Languages, and Applications, OOPSLA 2016.

[1]: https://github.com/smarr/SOMns
[2]: http://www.newspeaklanguage.org/
[3]: https://stefan-marr.de/papers/dls-marr-et-al-cross-language-compiler-benchmarking-are-we-fast-yet/
[4]: https://stefan-marr.de/downloads/dls16-marr-et-al-cross-language-compiler-benchmarking-are-we-fast-yet.pdf
[5]: https://2016.splashcon.org/event/splash-2016-oopsla-efficient-and-thread-safe-objects-for-dynamically-typed-languages
[6]: https://stefan-marr.de/downloads/manlang17-grimmer-et-al-applying-optimizations-for-dynamically-typed-languages-to-java.pdf
[7]: https://stefan-marr.de/downloads/dls17-pimas-et-al-garbage-collection-and-efficiency-in-dynamic-metacircular-runtimes.pdf
[8]: http://stefan-marr.de/downloads/tse18-chari-et-al-fully-reflective-execution-environments.pdf
[9]: http://stefan-marr.de/downloads/manlang18-aumayr-et-al-efficient-and-deterministic-record-and-replay-for-actor-languages.pdf
[10]: http://stefan-marr.de/downloads/oopsla18-daloze-et-al-parallelization-of-dynamic-languages-synchronizing-built-in-collections.pdf
[11]: http://stefan-marr.de/downloads/grace18-marr-et-al-newspeak-and-truffle-a-platform-for-grace.pdf
[12]: https://pure.tudelft.nl/portal/en/publications/specializing-a-metainterpreter(bc672ab9-d2bb-42d9-b67f-3f051868e3d7).html
[13]: https://github.com/densh/talks/blob/master/2018-06-16-interflow-preprint-v1.pdf
[14]: http://janvitek.org/pubs/dls18.pdf
[15]: https://fniephaus.com/2019/mplr19-graalsqueak.pdf
[16]: http://janvitek.org/pubs/oopsla20-cd.pdf
[17]: http://www.inf.puc-rio.br/~hgualandi/papers/Gualandi-2022-SCP.pdf
[18]: https://www.jstage.jst.go.jp/article/ipsjjip/30/0/30_679/_pdf
[19]: https://dl.acm.org/doi/abs/10.1145/3591244
[20]: https://dspace.cvut.cz/bitstream/handle/10467/109352/F8-BP-2023-Riha-Filip-thesis.pdf
[21]: https://stefan-marr.de/downloads/dls22-kaleba-et-al-analyzing-the-run-time-call-site-behavior-of-ruby-applications.pdf
[22]: https://dl.acm.org/doi/10.1145/3459898.3463903
[23]: https://prg.is.titech.ac.jp/wp-content/uploads/2023/03/izawa-phd-dissertation.pdf
[24]: https://stefan-marr.de/downloads/vmil22-ugawa-et-al-profile-guided-offline-optimization-of-hidden-class-graphs.pdf
[25]: https://stefan-marr.de/downloads/acmsac23-huang-et-al-optimizing-the-order-of-bytecode-handlers-in-interpreters-using-a-genetic-algorithm.pdf
[26]: https://drops.dagstuhl.de/opus/volltexte/2019/10796/pdf/LIPIcs-ECOOP-2019-4.pdf
[27]: http://www.jot.fm/issues/issue_2022_02/article2.pdf

[CD]: https://www.cs.purdue.edu/sss/projects/cdx/
[CDjs]: https://github.com/WebKit/webkit/tree/master/PerformanceTests/JetStream/cdjs
[DeltaBlue]: http://www.wolczko.com/java_benchmarking.html
[Havlak]: https://days2011.scala-lang.org/sites/days2011/files/ws3-1-Hundt.pdf
[Json]: https://github.com/ralfstx/minimal-json
[Richards]: http://www.cl.cam.ac.uk/~mr10/Bench.html
[SOM]: http://som-st.github.io/
[CLBG]: https://benchmarksgame-team.pages.debian.net/benchmarksgame/
[Crystal]: http://crystal-lang.org/