Devito: Fast Finite Difference Computation from Symbolic Specification
Devito is a software to implement optimised finite difference (FD) computation from high-level symbolic problem definitions. Starting from symbolic equations defined in SymPy, Devito employs automated code generation and just-in-time (JIT) compilation to execute FD kernels on multiple computer platforms.
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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.
The recommended way to install Devito uses the Conda package manager for installation of the necessary software dependencies. Please install either Anaconda or Miniconda using the instructions provided at the download links. You will need the Python 3.6 version.
To install Devito, including examples, tests and tutorial notebooks, follow these simple passes:
git clone https://github.com/opesci/devito.git cd devito conda env create -f environment.yml source activate devito pip install -e .
Alternatively, you can also install and run Devito via Docker:
# get the code git clone https://github.com/opesci/devito.git cd devito # run the tests docker-compose run devito /tests # start a jupyter notebook server on port 8888 docker-compose up devito # start a bash shell with devito docker-compose run devito /bin/bash
At the core of the Devito API are the so-called
Operator objects, which
allow the creation and execution of efficient FD kernels from SymPy
expressions. Examples of how to define operators are provided:
- A set of introductory notebook tutorials introducing the basic
features of Devito operators can be found under
examples/cfd. These tutorials cover a range of well-known examples from Computational Fluid Dynamics (CFD) and are based on the excellent introductory blog "CFD Python:12 steps to Navier-Stokes" by the Lorena A. Barba group. To run these, simply go into the tutorial directory and run
- A set of tutorial notebooks for seismic inversion examples is available in
- Example implementations of acoustic forward, adjoint, gradient and born
operators for use in full-waveform inversion (FWI) methods can be found in
- An advanced example of a Tilted Transverse Isotropy forward operator
for use in FWI can be found in
- A benchmark script for the acoustic and TTI forward operators can be
Devito's JIT compiler engine supports multiple backends, with provided
presets for the most common compiler toolchains. By default, Devito
will use the default GNU compiler
g++, but other toolchains may be
selected by setting the
DEVITO_ARCH environment variable to one of
the following values:
gnu- Standard GNU compiler toolchain
osx- Mac OSX compiler toolchain via
icpc- Intel compiler toolchain via
Thread parallel execution via OpenMP can also be enabled by setting
For the full list of available environment variables and their possible values, simply run:
from devito import print_defaults print_defaults()
Or with Docker, run:
docker-compose run devito /print-defaults
Devito supports two classes of performance optimizations:
- Flop-count optimizations - They aim to reduce the operation count of an FD kernel. These include, for example, code motion, factorization, and detection of cross-stencil redundancies. The flop-count optimizations are performed by routines built on top of SymPy, implemented in the Devito Symbolic Engine (DSE), a sub-module of Devito.
- Loop optimizations - Examples include SIMD vectorization and parallelism (via code annotations) and loop blocking. These are performed by the Devito Loop Engine (DLE), another sub-module of Devito.
Further, YASK is being integrated as a Devito backend, for optimized execution on Intel architectures.
Devito supports automatic auto-tuning of block sizes when loop blocking is
enabled. Enabling auto-tuning is simple: it can be done by passing the special
autotune=True to an
Operator. Auto-tuning parameters can be set
through the special environment variable
For more information on how to drive Devito for maximum run-time performance, see here or ask the developers on the Slack channel linked above.