Speeding up stencil computations on CPUs and GPUs
Run blazingly fast stencil codes on numpy arrays.
pystencils uses sympy to define stencil operations, that can be executed on numpy arrays. Exploiting the stencil structure makes pystencils run faster than normal numpy code and even as Cython and numba, as demonstrated in this notebook.
Here is a code snippet that computes the average of neighboring cells:
import pystencils as ps import numpy as np f, g = ps.fields("f, g : [2D]") stencil = ps.Assignment(g[0, 0], (f[1, 0] + f[-1, 0] + f[0, 1] + f[0, -1]) / 4) kernel = ps.create_kernel(stencil).compile() f_arr = np.random.rand(1000, 1000) g_arr = np.empty_like(f_arr) kernel(f=f_arr, g=g_arr)
pystencils is mostly used for numerical simulations using finite difference or finite volume methods. It comes with automatic finite difference discretization for PDEs:
c, v = ps.fields("c, v(2): [2D]") adv_diff_pde = ps.fd.transient(c) - ps.fd.diffusion(c, sp.symbols("D")) + ps.fd.advection(c, v) discretize = ps.fd.Discretization2ndOrder(dx=1, dt=0.01) discretization = discretize(adv_diff_pde)
pip install pystencils[interactive]
[interactive] you get a minimal version with very little dependencies.
gpu: use this if an NVIDIA GPU is available and CUDA is installed
opencl: basic OpenCL support (experimental)
alltrafos: pulls in additional dependencies for loop simplification e.g. libisl
bench_db: functionality to store benchmark result in object databases
interactive: installs dependencies to work in Jupyter including image I/O, plotting etc.
autodiff: enable derivation of adjoint kernels and generation of Torch/Tensorflow operations
doc: packages to build documentation
Options can be combined e.g.
pip install pystencils[interactive,gpu,doc]
Read the docs here and
check out the Jupyter notebooks in
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