/ devito Public

Code generation framework for automated finite difference computation

# devitocodes/devito

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# 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.

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

```>>> 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

`>>> op(t=timesteps)`

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:

``````# 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.

You may also be interested in TheMatrix -- a cross-architecture benchmarking framework showing the performance of several production-grade seismic operators implemented with Devito.

## 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.

Code generation framework for automated finite difference computation

v4.7.1 Latest
Aug 3, 2022

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