Metadata-Version: 2.4
Name: loopy
Version: 2025.2
Summary: A code generator for array-based code on CPUs and GPUs
Project-URL: Documentation, https://documen.tician.de/loopy
Project-URL: Homepage, https://github.com/inducer/loopy
Author-email: Andreas Kloeckner <inform@tiker.net>
License-Expression: MIT
License-File: LICENSE
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Other Audience
Classifier: Intended Audience :: Science/Research
Classifier: Natural Language :: English
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Visualization
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Utilities
Requires-Python: >=3.10
Requires-Dist: cgen>=2025.1
Requires-Dist: codepy>=2017.1
Requires-Dist: colorama
Requires-Dist: constantdict
Requires-Dist: genpy>=2016.1.2
Requires-Dist: islpy>=2025.2.5
Requires-Dist: mako
Requires-Dist: numpy>=1.19
Requires-Dist: pymbolic>=2024.2.2
Requires-Dist: pytools>=2025.2.2
Requires-Dist: typing-extensions>=4
Provides-Extra: fortran
Requires-Dist: fparser>=0.2.0; extra == 'fortran'
Requires-Dist: ply>=3.6; extra == 'fortran'
Provides-Extra: pyopencl
Requires-Dist: pyopencl>=2022.3; extra == 'pyopencl'
Description-Content-Type: text/x-rst

Loopy: Transformation-Based Generation of High-Performance CPU/GPU Code
=======================================================================

.. image:: https://gitlab.tiker.net/inducer/loopy/badges/main/pipeline.svg
    :alt: Gitlab Build Status
    :target: https://gitlab.tiker.net/inducer/loopy/commits/main
.. image:: https://github.com/inducer/loopy/actions/workflows/ci.yml/badge.svg
    :alt: Github Build Status
    :target: https://github.com/inducer/loopy/actions/workflows/ci.yml
.. image:: https://badge.fury.io/py/loopy.svg
    :alt: Python Package Index Release Page
    :target: https://pypi.org/project/loopy/
.. image:: https://zenodo.org/badge/20281732.svg
    :alt: Zenodo DOI for latest release
    :target: https://zenodo.org/doi/10.5281/zenodo.10672274

Loopy lets you easily generate the tedious, complicated code that is necessary
to get good performance out of GPUs and multi-core CPUs.
Loopy's core idea is that a computation should be described simply and then
*transformed* into a version that gets high performance. This transformation
takes place under user control, from within Python.

It can capture the following types of optimizations:

* Vector and multi-core parallelism in the OpenCL/CUDA model
* Data layout transformations (structure of arrays to array of structures)
* Loop unrolling
* Loop tiling with efficient handling of boundary cases
* Prefetching/copy optimizations
* Instruction level parallelism
* and many more!

Loopy targets array-type computations, such as the following:

* dense linear algebra,
* convolutions,
* n-body interactions,
* PDE solvers, such as finite element, finite difference, and
  Fast-Multipole-type computations.

It is not (and does not want to be) a general-purpose programming language.

Loopy is licensed under the liberal `MIT license
<https://en.wikipedia.org/wiki/MIT_License>`__ and free for commercial, academic,
and private use. All of Loopy's dependencies can be automatically installed from
the package index after using::

    pip install loopy

In addition, Loopy is compatible with and enhances
`pyopencl <https://mathema.tician.de/software/pyopencl>`__.

---

Places on the web related to Loopy:

* `Python Package Index <https://pypi.org/project/loopy>`__ (download releases)
* `Documentation <https://documen.tician.de/loopy>`__ (read how things work)
* `Github <https://github.com/inducer/loopy>`__ (get latest source code, file bugs)
* `Homepage <https://mathema.tician.de/software/loopy>`__
* `Benchmarks <https://documen.tician.de/loopy/benchmarks>`__
