pystencils 2.0b2

Speeding up stencil computations on CPUs and GPUs

pystencils

Pystencils is a symbolic domain-specific language and metaprogramming toolkit for writing high-performance numerical stencil kernels for a variety of hardware targets.

[!note] This is the code of repository of pystencils 2.x, the near-complete rework of the pystencils package. The legacy package pystencils 1.4 is being supported at the v1.x branch of this repository.

Installation

Pystencils can be installed from PyPI using pip, e.g.:

pip install pystencils~=2.0

For more information on installing pystencils, refer to the Installation Guide.

Example

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:

import pystencils as ps
import sympy as sp

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)

Documentation

Here's an overview of our documentation ressources:

• pystencils User Manual (master)
• pycodegen Index Page

Contributing

Please refer to the contribution guide for instructions on how to start contributing to pystencils.

Authors

Many thanks go to the contributors of pystencils.

Please cite us

If you use pystencils in a publication, please cite the following articles:

Overview:

• M. Bauer et al, Code Generation for Massively Parallel Phase-Field Simulations. Association for Computing Machinery, 2019. https://doi.org/10.1145/3295500.3356186

Performance Modelling:

• D. Ernst et al, Analytical performance estimation during code generation on modern GPUs. Journal of Parallel and Distributed Computing, 2023. https://doi.org/10.1016/j.jpdc.2022.11.003