Are We Fast Yet? Comparing Language Implementations with Objects, Closures, and Arrays
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, and arrays.
This is in contrast to other projects such as the Computer Language Benchmark game, which encourage finding the smartest possible way to express a problem in a language to achieve best performance.
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:
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.
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.
The initial publication describing the project is Cross-Language Compiler Benchmarking: Are We Fast Yet? and can be cited as follows:
Stefan Marr, Benoit Daloze, Hanspeter Mössenböck. 2016. Cross-Language Compiler Benchmarking: Are We Fast Yet? 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.
The graph below shows the results for the different implementations after warmup, to ensure peak performance is reported:
A detailed overview of the results is in docs/performance.md.
The benchmarks are listed below. A detailed analysis including metrics for the benchmarks is in docs/metrics.md.
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
Micro benchmarks are based on SOM Smalltalk 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.
NBody simulates the movement of planets in the solar system. It is derived from the Computer Languages Benchmark Game.
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.
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.
A list of languages we would definitely be interested in is on the issues tracker.
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
To obtain the code, benchmarks, and documentation, checkout the git repository:
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
executed like this:
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 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
To execute the benchmarks on all supported VMs, the following implementations are expected to be already available on the benchmark machine:
- GraalVM, expected to be available in
implementations/graalvm. Please see 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.
implementations folder contains wrapper scripts such as
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
-d gives more output during execution, and
--without-nice means that
nice tool enforcing high process priority is not used. We don't use it
here to avoid requiring root rights.
Note: The rebench.conf file specifies how and which benchmarks to execute. It also defines the arguments to be passed to the benchmarks.
Papers using this benchmark suite
Benoit Daloze, Stefan Marr, Daniele Bonetta, Hanspeter Mössenböck. 2016. Efficient and Thread-Safe Objects for Dynamically-Typed Languages In Proceedings of the 2016 ACM SIGPLAN International Conference on Object-Oriented Programming, Systems, Languages, and Applications (OOPSLA '16). ACM.