devito
DSL and compiler framework for automated finite-differences and stencil computation
Science Score: 54.0%
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Repository
DSL and compiler framework for automated finite-differences and stencil computation
Basic Info
- Host: GitHub
- Owner: devitocodes
- License: mit
- Language: Python
- Default Branch: main
- Homepage: http://www.devitoproject.org
- Size: 283 MB
Statistics
- Stars: 617
- Watchers: 31
- Forks: 238
- Open Issues: 132
- Releases: 47
Topics
Metadata Files
README.md
Devito: Fast Stencil Computation from Symbolic Specification
Devito is a Python package to implement optimized stencil computation (e.g., finite differences, image processing, machine learning) from high-level symbolic problem definitions. Devito builds on SymPy and employs automated code generation and just-in-time compilation to execute optimized computational kernels on several computer platforms, including CPUs, GPUs, and clusters thereof.
About Devito
Devito provides a functional language to implement sophisticated operators that can be made up of multiple stencil computations, boundary conditions, sparse operations (e.g., interpolation), and much more. A typical use case is explicit finite difference methods for approximating partial differential equations. For example, a 2D diffusion operator may be implemented with Devito as follows
```python
grid = Grid(shape=(10, 10)) f = TimeFunction(name='f', grid=grid, space_order=2) eqn = Eq(f.dt, 0.5 * f.laplace) op = Operator(Eq(f.forward, solve(eqn, f.forward))) ```
An Operator generates low-level code from an ordered collection of Eq (the
example above being for a single equation). This code may also be compiled and
executed
```python
op(t=timesteps, dt=dt) ```
There is virtually no limit to the complexity of an Operator -- the Devito
compiler will automatically analyze the input, detect and apply optimizations
(including single- and multi-node parallelism), and eventually generate code
with suitable loops and expressions.
Key features include:
- A functional language to express finite difference operators.
- Straightforward mechanisms to adjust the discretization.
- Constructs to express sparse operators (e.g., interpolation), classic linear operators (e.g., convolutions), and tensor contractions.
- Seamless support for boundary conditions and adjoint operators.
- A flexible API to define custom stencils, sub-domains, sub-sampling, and staggered grids.
- Generation of highly optimized parallel code (SIMD vectorization, CPU and GPU parallelism via OpenMP and OpenACC, multi-node parallelism via MPI, blocking, aggressive symbolic transformations for FLOP reduction, etc.).
- Distributed NumPy arrays over multi-node (MPI) domain decompositions.
- Inspection and customization of the generated code.
- Autotuning framework to ease performance tuning.
- Smooth integration with popular Python packages such as NumPy, SymPy, Dask, and SciPy, as well as machine learning frameworks such as TensorFlow and PyTorch.
Installation
The easiest way to try Devito is through Docker using the following commands: ```bash
get the code
git clone https://github.com/devitocodes/devito.git cd devito
start a jupyter notebook server on port 8888
docker-compose up devito
``
After running the last command above, the terminal will display a URL such as
https://127.0.0.1:8888/?token=XXX`. Copy-paste this URL into a browser window
to start a Jupyter notebook session where you can go
through the tutorials
provided with Devito or create your own notebooks.
See here for detailed installation instructions and other options. If you encounter a problem during installation, please see the installation issues we have seen in the past.
Resources
To learn how to use Devito, here is a good place to start, with lots of examples and tutorials.
The website also provides access to other information, including documentation and instructions for citing us.
Some FAQs are discussed here.
Performance
If you are interested in any of the following
- Generation of parallel code (CPU, GPU, multi-node via MPI);
- Performance tuning;
- Benchmarking operators;
then you should take a look at this README.
Get in touch
If you're using Devito, we would like to hear from you. Whether you are facing issues or just trying it out, join the conversation.
Interactive jupyter notebooks
The tutorial jupyter notebook are available interactively at the public binder jupyterhub.
Owner
- Name: Devito Codes
- Login: devitocodes
- Kind: organization
- Email: g.gorman@imperial.ac.uk
- Website: https://www.devitoproject.org/
- Repositories: 17
- Profile: https://github.com/devitocodes
Citation (CITATION.md)
# Citing Devito
If you publish results using Devito, we would be grateful if you would cite the following papers:
```bibtex
@article{devito-compiler,
author = {Luporini, Fabio and Louboutin, Mathias and Lange, Michael and Kukreja, Navjot and Witte, Philipp and H\"{u}ckelheim, Jan and Yount, Charles and Kelly, Paul H. J. and Herrmann, Felix J. and Gorman, Gerard J.},
title = {Architecture and Performance of Devito, a System for Automated Stencil Computation},
year = {2020},
issue_date = {March 2020},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {46},
number = {1},
issn = {0098-3500},
url = {https://doi.org/10.1145/3374916},
doi = {10.1145/3374916},
journal = {ACM Trans. Math. Softw.},
month = {apr},
articleno = {6},
numpages = {28},
keywords = {stencil, domain-specific language, symbolic processing, performance optimization, compiler, structured grid, Finite-difference method}
}
@article{devito-api,
author = {Louboutin, M. and Lange, M. and Luporini, F. and Kukreja, N. and Witte, P. A. and Herrmann, F. J. and Velesko, P. and Gorman, G. J.},
title = {Devito (v3.1.0): an embedded domain-specific language for finite differences and geophysical exploration},
journal = {Geoscientific Model Development},
volume = {12},
year = {2019},
number = {3},
pages = {1165--1187},
url = {https://www.geosci-model-dev.net/12/1165/2019/},
doi = {10.5194/gmd-12-1165-2019}
}
```
Additionally, if you use Devito for distributed runs, you may want to cite the following paper as well:
```bibtex
@misc{bisbas2024automatedmpixcodegeneration,
title={Automated MPI-X code generation for scalable finite-difference solvers},
author={Bisbas, George and Nelson, Rhodri and Louboutin, Mathias and Luporini, Fabio and Kelly, Paul H.J. and Gorman, Gerard},
booktitle={2025 IEEE International Parallel and Distributed Processing Symposium (IPDPS)},
title={Automated MPI-X Code Generation for Scalable Finite-Difference Solvers},
year={2025},
volume={},
number={},
pages={689-701},
keywords={Codes;Source coding;Parallel processing;Throughput;Mathematical models;Hybrid power systems;DSL;Kernel;Tuning;Finite difference methods;DSLs;finite-difference method;symbolic computation;stencil computation;MPI;distributed-memory parallelism;compilation;high-performance computing;CPUs;GPUs},
doi={10.1109/IPDPS64566.2025.00067}
}
```
## Citing a specific release of Devito through Zenodo
To cite a specific release of Devito through Zenodo, you can follow this link pointing to the latest release:
https://doi.org/10.5281/zenodo.597277 .
Committers
Last synced: almost 3 years ago
All Time
- Total Commits: 9,417
- Total Committers: 106
- Avg Commits per committer: 88.84
- Development Distribution Score (DDS): 0.519
Top Committers
| Name | Commits | |
|---|---|---|
| Fabio Luporini | f****2@i****k | 4,534 |
| mloubout | m****n@g****m | 1,022 |
| Michael Lange | m****e@i****k | 708 |
| Navjot Kukreja | n****k@g****m | 475 |
| George Bisbas | g****8@i****k | 385 |
| Rhodri Nelson | r****n@g****m | 334 |
| Fabio Luporini | f****o@g****m | 278 |
| FabioLuporini | f****o@d****m | 200 |
| wask | w****k@c****m | 137 |
| beterraba | t****r@g****m | 111 |
| Tim Burgess | t****b@d****m | 103 |
| Vincenzo Pandolfo | v****5@i****k | 89 |
| Mathias Louboutin | M****n@g****m | 85 |
| Vincenzo Pandolfo | p****e@g****m | 85 |
| ofmla | o****l@h****m | 65 |
| Gerard Gorman | g****n@i****k | 55 |
| Ken Hester | k****r@n****m | 55 |
| EdCaunt | e****5@i****k | 41 |
| vmickus | v****r@g****m | 41 |
| dependabot[bot] | 4****]@u****m | 39 |
| sheino | p****7@y****k | 36 |
| Victor Leite | l****d@g****m | 35 |
| jack-lascala | g****8@i****k | 30 |
| Chris Dinneen | c****n@g****m | 27 |
| maelso | m****o@d****r | 26 |
| Joao Speglich | s****j@g****m | 26 |
| Gabriel Sebastian von Conta | m****l@g****m | 25 |
| Peterson Nogueira | n****n@g****m | 21 |
| italoaug | i****1@g****m | 19 |
| Jaime Freire de Souza | j****a@g****m | 18 |
| and 76 more... | ||
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 4 months ago
All Time
- Total issues: 133
- Total pull requests: 814
- Average time to close issues: over 1 year
- Average time to close pull requests: about 1 month
- Total issue authors: 49
- Total pull request authors: 29
- Average comments per issue: 2.04
- Average comments per pull request: 1.61
- Merged pull requests: 593
- Bot issues: 1
- Bot pull requests: 90
Past Year
- Issues: 27
- Pull requests: 370
- Average time to close issues: about 1 month
- Average time to close pull requests: 4 days
- Issue authors: 16
- Pull request authors: 12
- Average comments per issue: 0.41
- Average comments per pull request: 1.4
- Merged pull requests: 260
- Bot issues: 0
- Bot pull requests: 57
Top Authors
Issue Authors
- FabioLuporini (23)
- mloubout (14)
- EdCaunt (14)
- deckerla (7)
- georgebisbas (6)
- vahid-honarbakhsh (6)
- ggorman (6)
- ofmla (4)
- rhodrin (3)
- jiwei08 (3)
- navjotk (3)
- ccuetom (3)
- jkwashbourne (3)
- ZoeLeibowitz (2)
- gfkdliucheng (2)
Pull Request Authors
- mloubout (255)
- FabioLuporini (209)
- dependabot[bot] (90)
- EdCaunt (81)
- georgebisbas (57)
- ZoeLeibowitz (25)
- JDBetteridge (24)
- ggorman (17)
- enwask (12)
- AndrewCheng827 (7)
- ccuetom (5)
- rhodrin (5)
- Copilot (3)
- AtilaSaraiva (3)
- nogueirapeterson (3)
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Packages
- Total packages: 1
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Total downloads:
- pypi 3,036 last-month
- Total dependent packages: 2
- Total dependent repositories: 6
- Total versions: 30
- Total maintainers: 2
pypi.org: devito
Finite Difference DSL for symbolic computation.
- Documentation: https://www.devitoproject.org/devito/index.html
- License: The MIT License (MIT) Copyright (c) 2016, Imperial College, London Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
-
Latest release: 4.8.19
published 6 months ago