Opesci-FD is a software package to automatically generate Finite Difference models from a high-level description of the model equations. It allows the rapid development, analysis and optimisation of propagator codes for use in seismic imaging.
Opesci-FD can be installed with:
pip install --local git+https://github.com/opesci/opesci-fd.git
This will install the latest version of the opesci python package
locally. To get the latests updates for your local copy simply add
--upgrade to the above command. For a developer checkout of the code
run:
git clone https://github.com/opesci/opesci-fd.git
cd opesci-fd; pip install -r requirements.txt
export PYTHONPATH=`pwd`:$PYTHONPATH
python setup.py build_clib --build-clib=`pwd`
An example of the high-level model description is provided under
tests/eigenwave3d.py. This script generates a stencil code that
models the propagation of an eigenwave on a unit cube by solving the
3D elastic wave equation on a staggered grid. To generate the model
code run:
python tests/eigenwave3d.py
This will generate the mode source code in tests/src/eigenwave3d.cpp.
The source code can be compiled and executed either manually or
automatically.
Opesci-FD provides automatic compilation and execution that allows developers to test their code directly from the Python environment. To compile the generted souce code:
python tests/eigenwave3d.py --compiler <cc>
where <cc> is either g++,clang or icpc, indicating which compiler to
use.Make sure your clang compiler support openmp
for multithreads program.
To compile and execute the above test case in parallel run:
python tests/eigenwave3d.py --compiler <cc> --execute --nthreads <nt>
where <nt> is the number of threads to use during execution.
For additional options please see:
python tests/eigenwave3d.py --help
If the PAPI library is found on your system during the initial build,
Opesci-FD can also provide profiling information, such as the achieved
number of MFlops/s during automated runs. To enable this feature
simply add the --profiling flag to the example command above. The
user can also supply a list of PAPI event names, for example
PAPI_TOT_CYC or PAPI_FP_OPS, via the --papi-events flag:
python tests/eigenwave3d.py -c g++ -x --n 4 --profiling --papi-events PAPI_TOT_CYC PAPI_FP_OPS
Please note that the availability of PAPI events is highly dependent on the hardware you are running on and the local PAPI install.
The generated source file can also be compiled by hand using the provided CMake file:
mkdir tests/build
cd tests/build
cmake ../src
make
bin/eigenwave3d
The outputs are grid spacings (dx, dy, dz), followed by L2 norms between numerical and analytical solutions for the stress and velocity fields. To switch off this output, change output_convergence from True to False in the Python test definition. Further parameter switches for controlling model input, the data type used (single of double precision or explicit vectorisation are also provided.
Here is a script to test the best tile size to get the best optimisation effect:
python tests/pluto_tile_test.py -b -s -l -- nthreads=4 tile_size="4 4 8"
The results will be in the "results" folder. The list "sizes" contains all the tile sizes that will be tested. This script depends on pybench.py