Skip to content

Latest commit

 

History

History
91 lines (61 loc) · 3.78 KB

README.md

File metadata and controls

91 lines (61 loc) · 3.78 KB
Raw

🟢🟩⬜🟩🟩
🟩⬜🟩⬜🟩
🟩🟩⬜🟩🟩
🟩⬜🟩⬜🟩
🟩🟩⬜🟩🟩

Burger.Bench

Benchmark the Burgers' Equation in Multiple Programming Languages

Motivation

Five years ago, Green Software Lab benchmarked 28 different programming languages with 10 distinct algorithms, where the benchmarking metrics were runtime, memory usage and energy consumption. The normalized results are shown in the figure below.

According to the paper and the repository, the 10 distinct algorithms used for benchmarking are shown in the table below, some of which are not computationally intensive.

Benchmark Description
binary-trees Allocate, traverse and deallocate many binary trees
fannkuch-redux Indexed access to tiny integer sequence
fasta Generate and write random DNA sequences
k-nucleotide Hashtable update and k-nucleotide strings
mandelbrot Generate Mandelbrot set portable bitmap file
n-body Double precision N-body simulation
pidigits Streaming arbitrary precision arithmetic
regex-redux Match DNA 8mers and substitute magic patterns
reverse-complement Read DNA sequences, write their reverse-complement
spectral-norm Eigenvalue using the power method

Therefore, in order to initially investigate what programming language is suitable for computational fluid dynamics, we implemented the programs for solving the Burgers' equation by the finite-volume method using the following programming languages:

  • C/C++
  • Fortran
  • Go
  • Julia
  • Python
  • Rust

Results

Time

For the time metrics, we recorded compile time and runtime. Compile time is largely independent of the grid resolution, while runtime is polynomial complexity.

FDM FVM
Compile
Execute

Memory

For memory metrics, we use linux's /proc/$pid/status to record the resident set size (VmRSS) and the virtual memory size (VmSize).

FDM FVM
VmRSS
VmSize

Contributing

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

If you have a suggestion to make this better, or find something incorrect, please fork the repository and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!

  1. Fork the Project
  2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
  3. Commit your Changes (git commit -m 'Add some AmazingFeature')
  4. Push to the Branch (git push origin feature/AmazingFeature)
  5. Open a Pull Request

License

Distributed under the GPL-3.0 License. See LICENSE.txt for more information.