https://github.com/niklas-heer/speed-comparison
A repo which compares the speed of different programming languages.
https://github.com/niklas-heer/speed-comparison
c clojure docker elixir golang hacktoberfest javascript julia nim-lang php programming-languages python ruby speed-comparison swift
Last synced: 18 days ago
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A repo which compares the speed of different programming languages.
- Host: GitHub
- URL: https://github.com/niklas-heer/speed-comparison
- Owner: niklas-heer
- License: mit
- Created: 2018-02-17T15:32:47.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2024-10-31T16:47:25.000Z (7 months ago)
- Last Synced: 2025-05-13T17:09:37.952Z (20 days ago)
- Topics: c, clojure, docker, elixir, golang, hacktoberfest, javascript, julia, nim-lang, php, programming-languages, python, ruby, speed-comparison, swift
- Language: Earthly
- Homepage: https://niklas-heer.github.io/speed-comparison
- Size: 12 MB
- Stars: 559
- Watchers: 16
- Forks: 82
- Open Issues: 37
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
---
# Speed comparison of programming languages
This projects tries to compare the speed of different programming languages.
In this project we don't really care about getting a precise calculation of pi. We only want to see how fast are the programming languages doing.
It uses an implementation of the [Leibniz formula for π](https://en.wikipedia.org/wiki/Leibniz_formula_for_%CF%80) to do the comparison.
Here is a video which explains how it works: [Calculating π by hand](https://www.youtube.com/watch?v=HrRMnzANHHs)
You can find the results here: https://niklas-heer.github.io/speed-comparison/
## Disclaimer
I'm no expert in all these languages, so take my results with a grain of salt.
Also the findings just show how good the languages can handle floating point operations, which is only one aspect of a programming language.
You are also welcome to contribute and help me fix my possible horrible code in some languages. :smile:
## Run it yourself
Everything is run by a Docker container and a bash script which envokes the programs.
To measure the execution time a [python package](https://pypi.python.org/pypi/lauda/1.2.0) is used.
### Requirements
- `Docker`
- [earthly](https://earthly.dev/)
### Run everything
Earthly allows to run everything with a single command:
```bash
earthly --config earthly-config.yml +all
```
This will run all tasks to collect all measurements and then run the analysis.
### Collect data
To collect data for all languages run:
```bash
earthly --config earthly-config.yml +collect-data
```
To collect data for a single languages run:
```bash
earthly --config earthly-config.yml +
```
### Analyse results
To generate the combined CSV out of all results use this command:
```bash
earthly --config earthly-config.yml +analysis
```
## FAQ
> Why do you also count reading a file and printing the output?
Because I think this is a more realistic scenario to compare speeds.
> Are the compile times included in the measurements?
No they are not included, because when running the program in the real world this would also be done before.
## Thanks
The list of Contributors is automatically generated.
### Contributors
Niklas Heer
Francesco Alemanno
Yu Zhu
Jerry Ling
Gaute Hope
frak0d
Jonathan Carroll
Sıddık AÇIL
Serg Gini
Gabriel Baraldi
Samuel
Joe Landman
Sandeep Kumar
mattn
Páll Haraldsson
Oscar Smith
Michael Abbott
Lorenzo Gabriele
LazyKernel
Chris Nuernberger
DirkieDurky
Gui Zhen Wei
RDPatrickTheDev
Proudust
Sergey Trofimov
Thomas A Caswell
Tomohiro
genmeblog
Yuta Imazu
Kohei Hosoki
### Special thanks
#### sharkdp
For creating [hyperfine](https://github.com/sharkdp/hyperfine) which is used for the fundamental benchmarking.
#### Thomas
This projects takes inspiration from [Thomas](https://www.thomaschristlieb.de) who did a similar comparison [on his blog](https://www.thomaschristlieb.de/performance-vergleich-zwischen-verschiedenen-programmiersprachen-und-systemen/).
