https://github.com/pyccel/pyccel-benchmarks
Up-to-date performance benchmark results for Pyccel
https://github.com/pyccel/pyccel-benchmarks
Last synced: 13 days ago
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Up-to-date performance benchmark results for Pyccel
- Host: GitHub
- URL: https://github.com/pyccel/pyccel-benchmarks
- Owner: pyccel
- License: mit
- Created: 2022-06-22T09:29:34.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2026-02-25T17:04:48.000Z (15 days ago)
- Last Synced: 2026-02-25T18:59:34.250Z (14 days ago)
- Language: Python
- Size: 6.09 MB
- Stars: 5
- Watchers: 2
- Forks: 2
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Benchmarks
Several tests are available for the user to benchmark Pyccel against other common accelerators, notably [pythran](https://pythran.readthedocs.io/en/latest/) and [numba](https://numba.pydata.org/).
The same code is used for all tests, only the decorators change.
The dependencies can be installed using the command `python3 -m pip install .`
The code can be executed by running the script `benchmarks/run_benchmarks.py`.
In order to test pyccel and/or pythran, configuration files must be provided. An example configuration for pythran is found in [`benchmarks/config.pythranrc`](./benchmarks/config.pythranrc). This configuration is the default pythran configuration with the following additional flags:
- `-O3`
- `-march=native`
- `-mtune=native`
- `-mavx`
- `-ffast-math`
Pyccel configurations valid for your machine can be generated using the following command (which may be adapted for another compiler family, see the [pyccel documentation](https://pyccel.github.io/pyccel/docs/compiler.html)):
```
pyccel --compiler-family intel --export-compiler-config pyccel_intel.json
```
This configuration can then be modified to include additional flags or use different compilers. The tests shown below add the following additional flags (which match the flags added to pythran):
- `-O3`
- `-march=native`
- `-mtune=native`
- `-mavx`
- `-ffast-math`
Additional options can be used with this script to add further comparisons, change the output format, or change what is generated.
Run `python3 benchmarks/run_benchmarks.py --help` for more details.
The results below are presented for the current state of the development branch of pyccel, as well as the most recent version of pyccel available on pypi.
A requirements.txt file providing the necessary packages to reproduce the tests run can be found in the `version_specific_results` folder.
The environment can be reproduced using the following commands:
```
python3 -m venv my_virtual_environment
source my_virtual_environment/bin/activate
pip3 install -r requirements.txt
```
## Tests used
The tests used can be found in the [benchmarks/tests](./benchmarks/tests) directory.
### Ackermann
A basic implementation of the Ackermann function which is one of the simplest and oldest examples of a total computable function that is not primitive recursive.
### Bellman Ford
An algorithm for solving the shortest path problem. The code is adapted from examples written by [J. Burkardt](https://people.sc.fsu.edu/~jburkardt/py_src/py_src.html)
### Djikstra
An algorithm for solving the shortest path problem. The code is adapted from examples written by [J. Burkardt](https://people.sc.fsu.edu/~jburkardt/py_src/py_src.html)
### Euler
Solves an ordinary differential equation using Euler's method. The code is adapted from examples written by [J. Burkardt](https://people.sc.fsu.edu/~jburkardt/py_src/py_src.html)
### Midpoint Explicit
Solves an ordinary differential equation using the explicit midpoint method. The code is adapted from examples written by [J. Burkardt](https://people.sc.fsu.edu/~jburkardt/py_src/py_src.html)
### Midpoint Fixed
Solves an ordinary differential equation using the implicit midpoint method with a fixed number of iterations. The code is adapted from examples written by [J. Burkardt](https://people.sc.fsu.edu/~jburkardt/py_src/py_src.html)
### RK4
Solves an ordinary differential equation using a fourth order Runge-Kutta method. The code is adapted from examples written by [J. Burkardt](https://people.sc.fsu.edu/~jburkardt/py_src/py_src.html)
### FD - Linear Convection
Solves a 1D linear convection problem using Finite Differences methods. The code is adapted from examples written by [L. A. Barba](https://lorenabarba.com/blog/cfd-python-12-steps-to-navier-stokes/)
### FD - Non-Linear Convection
Solves a 1D non-linear convection problem using Finite Differences methods. The code is adapted from examples written by [L. A. Barba](https://lorenabarba.com/blog/cfd-python-12-steps-to-navier-stokes/)
### FD - Poisson
Solves a 2D Poisson problem using Finite Differences methods. The code is adapted from examples written by [L. A. Barba](https://lorenabarba.com/blog/cfd-python-12-steps-to-navier-stokes/)
### FD - Laplace
Solves a 2D Laplace problem using Finite Differences methods. The code is adapted from examples written by [L. A. Barba](https://lorenabarba.com/blog/cfd-python-12-steps-to-navier-stokes/)
### MD
Runs a molecular dynamics simulation. The code is adapted from examples written by [J. Burkardt](https://people.sc.fsu.edu/~jburkardt/py_src/py_src.html)
### Splines
Evaluates a non-uniform spline saved as a class instance at a large number of test points. The code uses Algorithm A2.2 from the NURBS book (Piegl, Les, and Wayne Tiller. The NURBS book. Springer Science & Business Media, 2012.).
## Development branch results
### Performance Comparison (as of Thu Feb 26 14:43:18 UTC 2026)
## Compilation time
Algorithm | python | pythran_gnu | pythran_intel | numba | pyccel_gnu_c | pyccel_gnu_fortran | pyccel_intel_c | pyccel_intel_fortran
------------------------- | ------------------------- | ------------------------- | ------------------------- | ------------------------- | ------------------------- | ------------------------- | ------------------------- | -------------------------
Ackermann | - | 2.03 | 2.12 | 0.31 | 1.45 | 1.46 | 1.49 | 1.56
Bellman Ford | - | 3.39 | 3.71 | 1.09 | 1.63 | 1.63 | 1.69 | 1.73
Dijkstra | - | 2.34 | 2.68 | 1.16 | 1.70 | 1.71 | 1.74 | 1.85
Euler | - | 2.64 | 3.04 | 3.20 | 1.59 | 1.59 | 1.64 | 1.71
Midpoint Explicit | - | 2.97 | 3.44 | 3.57 | 1.83 | 1.84 | 1.87 | 1.94
Midpoint Fixed | - | 3.32 | 3.77 | 3.70 | 1.93 | 1.93 | 1.96 | 2.03
RK4 | - | 3.47 | 3.99 | 3.71 | 2.28 | 2.31 | 2.27 | 2.39
FD - L Convection | - | 2.32 | 2.73 | 2.50 | 1.55 | 1.55 | 1.59 | 1.67
FD - NL Convection | - | 3.39 | 3.59 | 2.62 | 1.57 | 1.55 | 1.58 | 1.67
FD - Poisson | - | 3.47 | 3.77 | 4.11 | 2.18 | 1.85 | 2.68 | 2.09
FD - Laplace | - | 6.98 | 7.61 | 5.35 | 1.96 | 2.00 | 1.92 | 2.14
M-D | - | 5.84 | 6.48 | 6.19 | 2.87 | 2.62 | 3.42 | 2.80
Splines | - | - | - | 0.75 | 2.24 | 1.85 | 2.74 | 2.01
## Execution time
Algorithm | python | pythran_gnu | pythran_intel | numba | pyccel_gnu_c | pyccel_gnu_fortran | pyccel_intel_c | pyccel_intel_fortran
------------------------- | ------------------------- | ------------------------- | ------------------------- | ------------------------- | ------------------------- | ------------------------- | ------------------------- | -------------------------
Ackermann (ms) | 481.00 | 2.70 | 3.68 | 8.45 | 1.33 | 1.32 | 3.70 | 9.40
Bellman Ford (ms) | 2080.00 | 4.57 | 3.47 | 4.07 | 3.30 | 3.23 | 5.49 | 4.18
Dijkstra (ms) | 5660.00 | 16.80 | 14.50 | 17.60 | 35.50 | 17.20 | 38.70 | 19.30
Euler (ms) | 3790.00 | 25.60 | 25.10 | 35.60 | 23.10 | 11.40 | 23.20 | 14.40
Midpoint Explicit (ms) | 7630.00 | 52.10 | 55.60 | 67.50 | 39.90 | 19.20 | 42.60 | 15.50
Midpoint Fixed (ms) | 37700.00 | 266.00 | 92.20 | 309.00 | 178.00 | 71.90 | 201.00 | 55.40
RK4 (ms) | 19300.00 | 156.00 | 34.80 | 136.00 | 85.40 | 32.30 | 91.10 | 27.80
FD - L Convection (ms) | 2320.00 | 1.51 | 1.60 | 2.82 | 4.28 | 1.77 | 4.54 | 1.49
FD - NL Convection (ms) | 2900.00 | 1.99 | 1.69 | 2.93 | 4.69 | 1.67 | 4.43 | 1.54
FD - Poisson (ms) | 6190.00 | 2.94 | 5.55 | 6.44 | 3.76 | 2.59 | 6.48 | 2.44
FD - Laplace (ms) | 706.00 | 67.20 | 105.00 | 189.00 | 150.00 | 60.40 | 192.00 | 59.10
M-D (ms) | 8560.00 | 35.30 | 50.20 | 56.70 | 106.00 | 62.00 | 88.70 | 90.60
Splines (ms) | 1740.00 | - | - | 18.10 | 13.60 | 17.70 | 15.10 | 27.90
## Python 3.10 results
### Performance Comparison (as of 2.1.0)
## Compilation time
Algorithm | python | pythran_gnu | pythran_intel | numba | pyccel_gnu_c | pyccel_gnu_fortran | pyccel_intel_c | pyccel_intel_fortran
------------------------- | ------------------------- | ------------------------- | ------------------------- | ------------------------- | ------------------------- | ------------------------- | ------------------------- | -------------------------
Ackermann | - | 1.93 | 1.98 | 0.30 | 1.28 | 1.30 | 1.31 | 1.37
Bellman Ford | - | 3.13 | 3.42 | 0.90 | 24.44 | 1.43 | 27.01 | 1.51
Dijkstra | - | 2.20 | 2.46 | 1.01 | 24.44 | 1.52 | 26.88 | 1.63
Euler | - | 2.43 | 2.74 | 3.18 | 24.29 | 1.42 | 26.73 | 1.47
Midpoint Explicit | - | 2.81 | 3.12 | 3.83 | 25.89 | 1.72 | 28.46 | 1.82
Midpoint Fixed | - | 3.20 | 3.47 | 3.67 | 24.60 | 1.67 | 26.93 | 1.75
RK4 | - | 3.38 | 3.74 | 3.77 | 25.19 | 2.09 | 27.62 | 2.13
FD - L Convection | - | 2.20 | 2.43 | 2.53 | 24.60 | 1.40 | 26.85 | 1.47
FD - NL Convection | - | 3.11 | 3.31 | 2.67 | 24.34 | 1.37 | 26.58 | 1.45
FD - Poisson | - | 3.17 | 3.36 | 4.35 | 24.56 | 1.64 | 26.66 | 1.81
FD - Laplace | - | 6.59 | 7.15 | 5.62 | 24.63 | 1.78 | 26.86 | 1.86
M-D | - | 5.69 | 5.74 | 6.08 | 25.30 | 2.45 | 27.70 | 2.53
Splines | - | - | - | 0.59 | 24.56 | 1.64 | 26.81 | 1.74
## Execution time
Algorithm | python | pythran_gnu | pythran_intel | numba | pyccel_gnu_c | pyccel_gnu_fortran | pyccel_intel_c | pyccel_intel_fortran
------------------------- | ------------------------- | ------------------------- | ------------------------- | ------------------------- | ------------------------- | ------------------------- | ------------------------- | -------------------------
Ackermann (ms) | 282.00 $\pm$ 3.00 | 7.10 $\pm$ 0.06 | 13.00 $\pm$ 0.00 | 19.10 $\pm$ 0.10 | 2.23 $\pm$ 0.01 | 2.27 $\pm$ 0.00 | 11.70 $\pm$ 0.10 | 15.30 $\pm$ 0.10
Bellman Ford (ms) | 1740.00 $\pm$ 20.00 | 4.59 $\pm$ 0.01 | 3.78 $\pm$ 0.08 | 4.57 $\pm$ 0.02 | 3.94 $\pm$ 0.10 | 3.21 $\pm$ 0.00 | 5.87 $\pm$ 0.02 | 3.53 $\pm$ 0.02
Dijkstra (ms) | 5020.00 $\pm$ 60.00 | 28.40 $\pm$ 0.70 | 23.10 $\pm$ 2.30 | 27.00 $\pm$ 0.40 | 43.90 $\pm$ 0.50 | 24.50 $\pm$ 0.50 | 58.50 $\pm$ 7.70 | 33.30 $\pm$ 2.80
Euler (ms) | 3210.00 $\pm$ 20.00 | 22.40 $\pm$ 0.40 | 22.80 $\pm$ 0.50 | 86.10 $\pm$ 2.90 | 19.50 $\pm$ 0.10 | 10.30 $\pm$ 0.30 | 22.80 $\pm$ 0.40 | 9.84 $\pm$ 0.36
Midpoint Explicit (ms) | 6440.00 $\pm$ 20.00 | 46.40 $\pm$ 0.90 | 45.20 $\pm$ 0.50 | 166.00 $\pm$ 1.00 | 38.70 $\pm$ 0.20 | 17.80 $\pm$ 0.20 | 41.70 $\pm$ 0.30 | 15.20 $\pm$ 0.40
Midpoint Fixed (ms) | 32700.00 $\pm$ 200.00 | 233.00 $\pm$ 3.00 | 82.30 $\pm$ 1.00 | 595.00 $\pm$ 11.00 | 171.00 $\pm$ 0.00 | 70.30 $\pm$ 4.60 | 180.00 $\pm$ 3.00 | 42.80 $\pm$ 0.40
RK4 (ms) | 16100.00 $\pm$ 100.00 | 131.00 $\pm$ 3.00 | 33.70 $\pm$ 0.50 | 309.00 $\pm$ 4.00 | 80.60 $\pm$ 1.40 | 26.60 $\pm$ 0.80 | 85.80 $\pm$ 3.50 | 29.90 $\pm$ 0.30
FD - L Convection (ms) | 2000.00 $\pm$ 10.00 | 1.31 $\pm$ 0.02 | 1.40 $\pm$ 0.00 | 2.14 $\pm$ 0.18 | 6.38 $\pm$ 0.04 | 1.49 $\pm$ 0.03 | 8.51 $\pm$ 0.06 | 1.40 $\pm$ 0.00
FD - NL Convection (ms) | 2530.00 $\pm$ 20.00 | 1.41 $\pm$ 0.01 | 1.33 $\pm$ 0.00 | 2.56 $\pm$ 0.12 | 6.07 $\pm$ 0.04 | 1.39 $\pm$ 0.02 | 8.58 $\pm$ 0.06 | 1.41 $\pm$ 0.00
FD - Poisson (ms) | 6170.00 $\pm$ 120.00 | 3.05 $\pm$ 0.01 | 6.17 $\pm$ 0.02 | 6.75 $\pm$ 0.06 | 8.62 $\pm$ 0.05 | 2.71 $\pm$ 0.01 | 13.30 $\pm$ 0.10 | 2.70 $\pm$ 0.01
FD - Laplace (ms) | 491.00 $\pm$ 5.00 | 64.60 $\pm$ 0.80 | 226.00 $\pm$ 2.00 | 214.00 $\pm$ 3.00 | 202.00 $\pm$ 1.00 | 55.40 $\pm$ 0.30 | 396.00 $\pm$ 5.00 | 60.70 $\pm$ 0.50
M-D (ms) | 14300.00 $\pm$ 0.00 | 25.10 $\pm$ 0.60 | 46.10 $\pm$ 0.60 | 53.40 $\pm$ 0.30 | 89.10 $\pm$ 0.10 | 55.50 $\pm$ 0.90 | 56.60 $\pm$ 0.20 | 54.70 $\pm$ 0.70
Splines (ms) | 1910.00 $\pm$ 20.00 | - | - | 17.70 $\pm$ 0.10 | 12.00 $\pm$ 0.10 | 17.00 $\pm$ 0.10 | 13.20 $\pm$ 0.10 | 26.50 $\pm$ 0.10
## Python 3.11 results
### Performance Comparison (as of 2.1.0)
## Compilation time
Algorithm | python | pythran_gnu | pythran_intel | numba | pyccel_gnu_c | pyccel_gnu_fortran | pyccel_intel_c | pyccel_intel_fortran
------------------------- | ------------------------- | ------------------------- | ------------------------- | ------------------------- | ------------------------- | ------------------------- | ------------------------- | -------------------------
Ackermann | - | 2.14 | 2.20 | 0.30 | 1.37 | 1.42 | 1.42 | 1.50
Bellman Ford | - | 3.31 | 3.66 | 0.98 | 26.13 | 1.56 | 29.68 | 1.66
Dijkstra | - | 2.27 | 2.60 | 1.10 | 26.69 | 1.71 | 30.31 | 1.82
Euler | - | 2.68 | 3.16 | 3.43 | 26.76 | 1.54 | 29.89 | 1.66
Midpoint Explicit | - | 2.95 | 3.40 | 3.73 | 26.66 | 1.79 | 30.51 | 1.90
Midpoint Fixed | - | 3.26 | 3.73 | 3.89 | 26.34 | 1.81 | 30.12 | 1.92
RK4 | - | 3.35 | 3.81 | 3.75 | 26.53 | 2.18 | 29.92 | 2.27
FD - L Convection | - | 2.26 | 2.68 | 2.41 | 25.85 | 1.47 | 29.48 | 1.57
FD - NL Convection | - | 3.21 | 3.55 | 2.59 | 25.72 | 1.47 | 29.27 | 1.56
FD - Poisson | - | 3.39 | 3.78 | 4.08 | 25.99 | 1.75 | 29.56 | 2.00
FD - Laplace | - | 7.00 | 7.77 | 5.33 | 26.41 | 1.89 | 29.77 | 2.05
M-D | - | 6.27 | 6.70 | 6.59 | 26.62 | 2.50 | 30.29 | 2.65
Splines | - | - | - | 0.65 | 25.91 | 1.75 | 29.93 | 1.94
## Execution time
Algorithm | python | pythran_gnu | pythran_intel | numba | pyccel_gnu_c | pyccel_gnu_fortran | pyccel_intel_c | pyccel_intel_fortran
------------------------- | ------------------------- | ------------------------- | ------------------------- | ------------------------- | ------------------------- | ------------------------- | ------------------------- | -------------------------
Ackermann (ms) | 430.00 $\pm$ 8.00 | 2.95 $\pm$ 0.16 | 3.67 $\pm$ 0.01 | 9.26 $\pm$ 0.47 | 1.25 $\pm$ 0.02 | 1.33 $\pm$ 0.01 | 4.06 $\pm$ 0.03 | 10.00 $\pm$ 0.50
Bellman Ford (ms) | 1740.00 $\pm$ 20.00 | 4.51 $\pm$ 0.01 | 3.65 $\pm$ 0.13 | 4.05 $\pm$ 0.02 | 4.06 $\pm$ 0.02 | 3.28 $\pm$ 0.03 | 5.19 $\pm$ 0.02 | 3.84 $\pm$ 0.01
Dijkstra (ms) | 4890.00 $\pm$ 40.00 | 20.00 $\pm$ 0.20 | 16.50 $\pm$ 0.40 | 20.80 $\pm$ 0.80 | 38.70 $\pm$ 0.50 | 19.80 $\pm$ 0.40 | 51.70 $\pm$ 0.60 | 22.70 $\pm$ 0.30
Euler (ms) | 3350.00 $\pm$ 90.00 | 25.80 $\pm$ 0.40 | 26.30 $\pm$ 0.30 | 35.80 $\pm$ 0.30 | 24.00 $\pm$ 4.20 | 11.40 $\pm$ 0.60 | 24.50 $\pm$ 0.40 | 14.90 $\pm$ 0.30
Midpoint Explicit (ms) | 6830.00 $\pm$ 70.00 | 52.90 $\pm$ 1.90 | 51.50 $\pm$ 2.40 | 67.50 $\pm$ 0.50 | 41.80 $\pm$ 4.50 | 19.70 $\pm$ 0.50 | 45.50 $\pm$ 4.20 | 16.30 $\pm$ 0.60
Midpoint Fixed (ms) | 34400.00 $\pm$ 200.00 | 269.00 $\pm$ 1.00 | 94.50 $\pm$ 0.90 | 308.00 $\pm$ 5.00 | 183.00 $\pm$ 1.00 | 73.20 $\pm$ 1.30 | 196.00 $\pm$ 3.00 | 55.70 $\pm$ 0.50
RK4 (ms) | 16900.00 $\pm$ 100.00 | 154.00 $\pm$ 3.00 | 34.80 $\pm$ 0.30 | 123.00 $\pm$ 2.00 | 89.40 $\pm$ 0.70 | 32.10 $\pm$ 0.40 | 93.70 $\pm$ 0.50 | 28.30 $\pm$ 0.40
FD - L Convection (ms) | 2130.00 $\pm$ 20.00 | 1.57 $\pm$ 0.07 | 1.51 $\pm$ 0.04 | 2.84 $\pm$ 0.04 | 5.80 $\pm$ 0.02 | 1.59 $\pm$ 0.11 | 8.20 $\pm$ 0.19 | 1.51 $\pm$ 0.01
FD - NL Convection (ms) | 2660.00 $\pm$ 20.00 | 2.04 $\pm$ 0.03 | 1.78 $\pm$ 0.01 | 2.88 $\pm$ 0.02 | 5.09 $\pm$ 0.03 | 1.53 $\pm$ 0.04 | 8.28 $\pm$ 0.18 | 1.40 $\pm$ 0.03
FD - Poisson (ms) | 5880.00 $\pm$ 100.00 | 2.95 $\pm$ 0.04 | 5.51 $\pm$ 0.06 | 6.57 $\pm$ 0.07 | 10.40 $\pm$ 0.30 | 2.57 $\pm$ 0.04 | 18.30 $\pm$ 0.10 | 2.54 $\pm$ 0.02
FD - Laplace (ms) | 628.00 $\pm$ 6.00 | 63.50 $\pm$ 0.50 | 103.00 $\pm$ 0.00 | 189.00 $\pm$ 1.00 | 204.00 $\pm$ 1.00 | 56.80 $\pm$ 0.80 | 355.00 $\pm$ 4.00 | 55.10 $\pm$ 0.30
M-D (ms) | 14300.00 $\pm$ 100.00 | 34.10 $\pm$ 0.20 | 50.30 $\pm$ 0.20 | 57.40 $\pm$ 1.60 | 105.00 $\pm$ 0.00 | 62.30 $\pm$ 0.20 | 91.90 $\pm$ 0.10 | 90.70 $\pm$ 0.10
Splines (ms) | 1680.00 $\pm$ 40.00 | - | - | 18.10 $\pm$ 0.10 | 14.10 $\pm$ 0.10 | 17.70 $\pm$ 0.00 | 15.10 $\pm$ 0.00 | 27.70 $\pm$ 0.20
## Python 3.12 results
### Performance Comparison (as of 2.1.0)
## Compilation time
Algorithm | python | pythran_gnu | pythran_intel | numba | pyccel_gnu_c | pyccel_gnu_fortran | pyccel_intel_c | pyccel_intel_fortran
------------------------- | ------------------------- | ------------------------- | ------------------------- | ------------------------- | ------------------------- | ------------------------- | ------------------------- | -------------------------
Ackermann | - | 2.00 | 2.20 | 0.31 | 1.43 | 1.45 | 1.49 | 1.54
Bellman Ford | - | 3.42 | 3.71 | 1.06 | 26.37 | 1.59 | 29.78 | 1.69
Dijkstra | - | 2.34 | 2.70 | 1.14 | 26.38 | 1.70 | 29.74 | 1.83
Euler | - | 2.59 | 3.05 | 3.25 | 26.02 | 1.57 | 29.70 | 1.67
Midpoint Explicit | - | 2.93 | 3.44 | 3.57 | 26.28 | 1.81 | 29.93 | 1.90
Midpoint Fixed | - | 3.35 | 3.82 | 3.73 | 26.39 | 1.89 | 29.93 | 2.02
RK4 | - | 3.49 | 4.06 | 3.75 | 26.76 | 2.28 | 30.48 | 2.36
FD - L Convection | - | 2.35 | 2.71 | 2.50 | 26.02 | 1.49 | 29.74 | 1.63
FD - NL Convection | - | 3.40 | 3.59 | 2.62 | 25.98 | 1.53 | 29.67 | 1.63
FD - Poisson | - | 3.50 | 3.83 | 4.16 | 26.14 | 1.79 | 29.76 | 2.03
FD - Laplace | - | 6.94 | 7.77 | 5.38 | 26.41 | 1.97 | 29.98 | 2.12
M-D | - | 5.90 | 6.46 | 6.20 | 26.82 | 2.59 | 30.42 | 2.77
Splines | - | - | - | 0.71 | 26.23 | 1.81 | 29.92 | 1.96
## Execution time
Algorithm | python | pythran_gnu | pythran_intel | numba | pyccel_gnu_c | pyccel_gnu_fortran | pyccel_intel_c | pyccel_intel_fortran
------------------------- | ------------------------- | ------------------------- | ------------------------- | ------------------------- | ------------------------- | ------------------------- | ------------------------- | -------------------------
Ackermann (ms) | 439.00 $\pm$ 7.00 | 2.73 $\pm$ 0.08 | 3.66 $\pm$ 0.01 | 9.50 $\pm$ 0.37 | 1.33 $\pm$ 0.01 | 1.28 $\pm$ 0.00 | 4.06 $\pm$ 0.02 | 10.00 $\pm$ 0.40
Bellman Ford (ms) | 2150.00 $\pm$ 40.00 | 4.59 $\pm$ 0.02 | 3.55 $\pm$ 0.09 | 4.06 $\pm$ 0.03 | 4.68 $\pm$ 0.02 | 3.25 $\pm$ 0.02 | 5.19 $\pm$ 0.14 | 4.17 $\pm$ 0.02
Dijkstra (ms) | 5850.00 $\pm$ 50.00 | 19.20 $\pm$ 0.10 | 16.60 $\pm$ 0.20 | 20.90 $\pm$ 0.40 | 37.80 $\pm$ 0.50 | 18.70 $\pm$ 0.20 | 50.70 $\pm$ 0.60 | 21.30 $\pm$ 0.20
Euler (ms) | 3840.00 $\pm$ 50.00 | 26.20 $\pm$ 0.40 | 26.20 $\pm$ 1.00 | 36.20 $\pm$ 0.40 | 22.30 $\pm$ 0.30 | 11.00 $\pm$ 0.40 | 24.00 $\pm$ 0.60 | 15.30 $\pm$ 0.40
Midpoint Explicit (ms) | 7800.00 $\pm$ 100.00 | 52.20 $\pm$ 0.30 | 51.80 $\pm$ 2.00 | 68.40 $\pm$ 1.50 | 39.90 $\pm$ 0.40 | 19.20 $\pm$ 0.40 | 44.00 $\pm$ 0.50 | 16.00 $\pm$ 0.40
Midpoint Fixed (ms) | 38400.00 $\pm$ 100.00 | 267.00 $\pm$ 1.00 | 92.90 $\pm$ 0.60 | 345.00 $\pm$ 102.00 | 183.00 $\pm$ 0.00 | 72.80 $\pm$ 0.80 | 196.00 $\pm$ 2.00 | 50.50 $\pm$ 0.30
RK4 (ms) | 19300.00 $\pm$ 100.00 | 153.00 $\pm$ 2.00 | 36.00 $\pm$ 0.80 | 124.00 $\pm$ 2.00 | 89.80 $\pm$ 2.60 | 32.40 $\pm$ 0.40 | 94.30 $\pm$ 0.70 | 29.80 $\pm$ 0.30
FD - L Convection (ms) | 2370.00 $\pm$ 20.00 | 1.54 $\pm$ 0.03 | 1.62 $\pm$ 0.02 | 2.83 $\pm$ 0.01 | 5.80 $\pm$ 0.02 | 1.66 $\pm$ 0.05 | 8.26 $\pm$ 0.20 | 1.33 $\pm$ 0.05
FD - NL Convection (ms) | 3030.00 $\pm$ 90.00 | 1.85 $\pm$ 0.03 | 1.65 $\pm$ 0.03 | 2.90 $\pm$ 0.06 | 5.10 $\pm$ 0.02 | 1.51 $\pm$ 0.01 | 8.24 $\pm$ 0.24 | 1.38 $\pm$ 0.04
FD - Poisson (ms) | 6630.00 $\pm$ 130.00 | 3.03 $\pm$ 0.16 | 5.35 $\pm$ 0.07 | 6.63 $\pm$ 0.04 | 10.50 $\pm$ 0.00 | 2.62 $\pm$ 0.04 | 18.70 $\pm$ 0.10 | 2.57 $\pm$ 0.04
FD - Laplace (ms) | 651.00 $\pm$ 19.00 | 67.70 $\pm$ 0.50 | 106.00 $\pm$ 1.00 | 190.00 $\pm$ 1.00 | 205.00 $\pm$ 1.00 | 58.80 $\pm$ 2.40 | 348.00 $\pm$ 1.00 | 56.90 $\pm$ 1.90
M-D (ms) | 16500.00 $\pm$ 300.00 | 35.20 $\pm$ 0.20 | 50.40 $\pm$ 0.20 | 57.00 $\pm$ 0.40 | 106.00 $\pm$ 0.00 | 62.40 $\pm$ 0.20 | 91.10 $\pm$ 0.10 | 90.70 $\pm$ 0.10
Splines (ms) | 1950.00 $\pm$ 40.00 | - | - | 17.80 $\pm$ 0.20 | 14.20 $\pm$ 0.10 | 17.80 $\pm$ 0.20 | 15.30 $\pm$ 0.00 | 27.90 $\pm$ 0.00
## Python 3.13 results
### Performance Comparison (as of 2.1.0)
## Compilation time
Algorithm | python | pythran_gnu | pythran_intel | numba | pyccel_gnu_c | pyccel_gnu_fortran | pyccel_intel_c | pyccel_intel_fortran
------------------------- | ------------------------- | ------------------------- | ------------------------- | ------------------------- | ------------------------- | ------------------------- | ------------------------- | -------------------------
Ackermann | - | 2.01 | 2.29 | 0.28 | 1.44 | 1.49 | 1.50 | 1.54
Bellman Ford | - | 3.36 | 3.69 | 0.97 | 25.97 | 1.52 | 27.76 | 1.64
Dijkstra | - | 2.37 | 2.66 | 1.02 | 25.98 | 1.64 | 29.50 | 1.84
Euler | - | 2.63 | 2.99 | 3.18 | 25.03 | 1.48 | 27.46 | 1.54
Midpoint Explicit | - | 2.83 | 3.17 | 3.35 | 25.61 | 1.67 | 28.03 | 1.73
Midpoint Fixed | - | 3.14 | 3.51 | 3.47 | 25.50 | 1.76 | 28.09 | 1.82
RK4 | - | 3.51 | 3.88 | 3.59 | 26.31 | 2.17 | 29.18 | 2.24
FD - L Convection | - | 2.26 | 2.52 | 2.42 | 25.41 | 1.47 | 28.10 | 1.54
FD - NL Convection | - | 3.26 | 3.42 | 2.52 | 25.40 | 1.44 | 28.14 | 1.53
FD - Poisson | - | 3.32 | 3.52 | 4.08 | 25.53 | 1.82 | 28.13 | 1.93
FD - Laplace | - | 7.00 | 7.52 | 5.30 | 25.74 | 1.87 | 28.34 | 1.96
M-D | - | 5.80 | 6.01 | 5.80 | 25.79 | 2.42 | 28.42 | 2.51
Splines | - | - | - | 0.62 | 25.82 | 1.73 | 27.87 | 1.83
## Execution time
Algorithm | python | pythran_gnu | pythran_intel | numba | pyccel_gnu_c | pyccel_gnu_fortran | pyccel_intel_c | pyccel_intel_fortran
------------------------- | ------------------------- | ------------------------- | ------------------------- | ------------------------- | ------------------------- | ------------------------- | ------------------------- | -------------------------
Ackermann (ms) | 335.00 $\pm$ 7.00 | 6.90 $\pm$ 0.04 | 13.00 $\pm$ 0.00 | 19.20 $\pm$ 0.10 | 2.15 $\pm$ 0.02 | 2.26 $\pm$ 0.01 | 11.70 $\pm$ 0.10 | 15.30 $\pm$ 0.10
Bellman Ford (ms) | 2020.00 $\pm$ 60.00 | 4.63 $\pm$ 0.01 | 3.71 $\pm$ 0.06 | 4.62 $\pm$ 0.05 | 4.07 $\pm$ 0.19 | 3.24 $\pm$ 0.01 | 5.89 $\pm$ 0.01 | 3.56 $\pm$ 0.01
Dijkstra (ms) | 5180.00 $\pm$ 40.00 | 35.20 $\pm$ 0.40 | 29.40 $\pm$ 1.40 | 36.00 $\pm$ 0.70 | 53.80 $\pm$ 0.90 | 31.10 $\pm$ 1.40 | 61.00 $\pm$ 0.50 | 40.20 $\pm$ 0.60
Euler (ms) | 3530.00 $\pm$ 40.00 | 24.80 $\pm$ 0.70 | 24.80 $\pm$ 0.30 | 87.80 $\pm$ 3.70 | 21.40 $\pm$ 0.20 | 11.10 $\pm$ 0.20 | 23.80 $\pm$ 0.30 | 11.10 $\pm$ 0.20
Midpoint Explicit (ms) | 7150.00 $\pm$ 70.00 | 46.60 $\pm$ 0.30 | 46.50 $\pm$ 0.70 | 166.00 $\pm$ 1.00 | 39.00 $\pm$ 0.30 | 17.90 $\pm$ 0.60 | 41.20 $\pm$ 0.50 | 14.80 $\pm$ 0.40
Midpoint Fixed (ms) | 35200.00 $\pm$ 200.00 | 232.00 $\pm$ 3.00 | 82.40 $\pm$ 0.50 | 597.00 $\pm$ 9.00 | 172.00 $\pm$ 0.00 | 68.70 $\pm$ 0.50 | 183.00 $\pm$ 2.00 | 44.60 $\pm$ 1.40
RK4 (ms) | 17500.00 $\pm$ 200.00 | 131.00 $\pm$ 2.00 | 35.20 $\pm$ 1.40 | 311.00 $\pm$ 5.00 | 80.80 $\pm$ 0.60 | 28.20 $\pm$ 0.90 | 85.60 $\pm$ 1.80 | 30.90 $\pm$ 0.30
FD - L Convection (ms) | 2240.00 $\pm$ 30.00 | 1.37 $\pm$ 0.01 | 1.46 $\pm$ 0.00 | 2.11 $\pm$ 0.20 | 6.32 $\pm$ 0.02 | 1.42 $\pm$ 0.01 | 8.51 $\pm$ 0.07 | 1.41 $\pm$ 0.00
FD - NL Convection (ms) | 2900.00 $\pm$ 70.00 | 1.40 $\pm$ 0.00 | 1.40 $\pm$ 0.00 | 2.49 $\pm$ 0.06 | 6.07 $\pm$ 0.04 | 1.39 $\pm$ 0.00 | 8.51 $\pm$ 0.06 | 1.42 $\pm$ 0.00
FD - Poisson (ms) | 6070.00 $\pm$ 110.00 | 3.10 $\pm$ 0.02 | 6.22 $\pm$ 0.02 | 6.78 $\pm$ 0.05 | 8.57 $\pm$ 0.03 | 2.72 $\pm$ 0.02 | 13.30 $\pm$ 0.10 | 2.70 $\pm$ 0.01
FD - Laplace (ms) | 559.00 $\pm$ 5.00 | 64.40 $\pm$ 0.70 | 229.00 $\pm$ 3.00 | 213.00 $\pm$ 2.00 | 203.00 $\pm$ 1.00 | 55.50 $\pm$ 0.50 | 398.00 $\pm$ 2.00 | 61.10 $\pm$ 0.80
M-D (ms) | 15100.00 $\pm$ 200.00 | 25.00 $\pm$ 0.70 | 46.00 $\pm$ 0.20 | 53.60 $\pm$ 0.30 | 89.40 $\pm$ 0.00 | 56.10 $\pm$ 0.40 | 57.00 $\pm$ 0.60 | 54.40 $\pm$ 0.30
Splines (ms) | 1880.00 $\pm$ 10.00 | - | - | 18.20 $\pm$ 0.70 | 12.00 $\pm$ 0.10 | 17.10 $\pm$ 0.20 | 13.40 $\pm$ 0.20 | 26.70 $\pm$ 0.10
## Python 3.14 results
### Performance Comparison (as of 2.1.0)
## Compilation time
Algorithm | python | pythran_gnu | pythran_intel | numba | pyccel_gnu_c | pyccel_gnu_fortran | pyccel_intel_c | pyccel_intel_fortran
------------------------- | ------------------------- | ------------------------- | ------------------------- | ------------------------- | ------------------------- | ------------------------- | ------------------------- | -------------------------
Ackermann | - | 2.10 | 2.37 | 0.31 | 1.47 | 1.48 | 1.51 | 1.61
Bellman Ford | - | 3.41 | 3.72 | 0.98 | 26.69 | 1.65 | 30.18 | 1.74
Dijkstra | - | 2.34 | 2.74 | 1.11 | 26.98 | 1.73 | 30.16 | 1.87
Euler | - | 2.64 | 3.05 | 3.18 | 26.30 | 1.60 | 29.98 | 1.71
Midpoint Explicit | - | 2.95 | 3.45 | 3.54 | 26.57 | 1.84 | 30.42 | 1.93
Midpoint Fixed | - | 3.43 | 3.80 | 3.70 | 26.84 | 1.93 | 30.51 | 2.08
RK4 | - | 3.55 | 3.99 | 3.64 | 26.85 | 2.33 | 30.66 | 2.38
FD - L Convection | - | 2.38 | 2.69 | 2.46 | 26.29 | 1.55 | 29.90 | 1.67
FD - NL Convection | - | 3.59 | 3.66 | 2.62 | 26.22 | 1.56 | 30.05 | 1.69
FD - Poisson | - | 3.52 | 3.76 | 4.15 | 26.51 | 1.85 | 30.26 | 2.08
FD - Laplace | - | 7.16 | 7.87 | 5.35 | 26.74 | 2.04 | 30.38 | 2.18
M-D | - | 5.95 | 6.47 | 6.16 | 27.00 | 2.60 | 30.86 | 2.77
Splines | - | - | - | 0.71 | 26.45 | 1.86 | 30.20 | 2.00
## Execution time
Algorithm | python | pythran_gnu | pythran_intel | numba | pyccel_gnu_c | pyccel_gnu_fortran | pyccel_intel_c | pyccel_intel_fortran
------------------------- | ------------------------- | ------------------------- | ------------------------- | ------------------------- | ------------------------- | ------------------------- | ------------------------- | -------------------------
Ackermann (ms) | 466.00 $\pm$ 6.00 | 2.71 $\pm$ 0.03 | 3.67 $\pm$ 0.01 | 9.32 $\pm$ 0.48 | 1.23 $\pm$ 0.00 | 1.28 $\pm$ 0.00 | 3.78 $\pm$ 0.10 | 10.50 $\pm$ 0.10
Bellman Ford (ms) | 2060.00 $\pm$ 40.00 | 4.52 $\pm$ 0.01 | 3.59 $\pm$ 0.09 | 4.06 $\pm$ 0.03 | 4.06 $\pm$ 0.02 | 3.27 $\pm$ 0.02 | 5.16 $\pm$ 0.03 | 4.17 $\pm$ 0.01
Dijkstra (ms) | 5070.00 $\pm$ 60.00 | 18.90 $\pm$ 0.20 | 16.70 $\pm$ 0.30 | 21.70 $\pm$ 0.50 | 44.70 $\pm$ 0.70 | 18.30 $\pm$ 0.40 | 47.20 $\pm$ 0.30 | 21.80 $\pm$ 0.50
Euler (ms) | 3800.00 $\pm$ 50.00 | 25.50 $\pm$ 0.30 | 25.70 $\pm$ 0.40 | 36.30 $\pm$ 0.60 | 24.00 $\pm$ 4.10 | 10.90 $\pm$ 0.30 | 24.30 $\pm$ 0.50 | 15.00 $\pm$ 0.40
Midpoint Explicit (ms) | 7880.00 $\pm$ 400.00 | 52.30 $\pm$ 0.60 | 51.00 $\pm$ 0.60 | 68.30 $\pm$ 0.60 | 40.10 $\pm$ 0.40 | 19.00 $\pm$ 0.30 | 43.90 $\pm$ 0.50 | 16.00 $\pm$ 0.50
Midpoint Fixed (ms) | 38500.00 $\pm$ 200.00 | 267.00 $\pm$ 1.00 | 93.10 $\pm$ 0.90 | 310.00 $\pm$ 2.00 | 184.00 $\pm$ 1.00 | 72.90 $\pm$ 0.40 | 198.00 $\pm$ 5.00 | 51.50 $\pm$ 0.60
RK4 (ms) | 19600.00 $\pm$ 200.00 | 158.00 $\pm$ 3.00 | 35.80 $\pm$ 0.60 | 132.00 $\pm$ 1.00 | 90.90 $\pm$ 0.40 | 31.70 $\pm$ 0.40 | 94.10 $\pm$ 0.90 | 27.90 $\pm$ 0.20
FD - L Convection (ms) | 2640.00 $\pm$ 20.00 | 1.66 $\pm$ 0.03 | 1.63 $\pm$ 0.03 | 2.85 $\pm$ 0.04 | 6.08 $\pm$ 0.05 | 1.53 $\pm$ 0.00 | 8.21 $\pm$ 0.27 | 1.35 $\pm$ 0.06
FD - NL Convection (ms) | 3300.00 $\pm$ 40.00 | 2.00 $\pm$ 0.02 | 1.67 $\pm$ 0.04 | 2.89 $\pm$ 0.06 | 5.11 $\pm$ 0.02 | 1.54 $\pm$ 0.05 | 8.23 $\pm$ 0.22 | 1.39 $\pm$ 0.02
FD - Poisson (ms) | 6760.00 $\pm$ 90.00 | 2.96 $\pm$ 0.07 | 5.51 $\pm$ 0.03 | 6.61 $\pm$ 0.05 | 10.60 $\pm$ 0.00 | 2.61 $\pm$ 0.02 | 18.80 $\pm$ 0.00 | 2.53 $\pm$ 0.02
FD - Laplace (ms) | 639.00 $\pm$ 14.00 | 63.80 $\pm$ 0.40 | 104.00 $\pm$ 0.00 | 191.00 $\pm$ 1.00 | 206.00 $\pm$ 1.00 | 59.00 $\pm$ 2.40 | 350.00 $\pm$ 1.00 | 55.70 $\pm$ 0.50
M-D (ms) | 16300.00 $\pm$ 200.00 | 34.50 $\pm$ 0.20 | 50.00 $\pm$ 0.10 | 56.90 $\pm$ 0.10 | 106.00 $\pm$ 0.00 | 62.40 $\pm$ 0.20 | 91.40 $\pm$ 0.10 | 90.60 $\pm$ 0.10
Splines (ms) | 1970.00 $\pm$ 30.00 | - | - | 16.50 $\pm$ 0.10 | 14.10 $\pm$ 0.00 | 17.70 $\pm$ 0.00 | 15.30 $\pm$ 0.10 | 27.80 $\pm$ 0.10
