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/workflows/CI/badge.svg?branch=main&event=push :alt: Github Build Status :target: https://github.com/inducer/loopy/actions?query=branch%3Amain+workflow%3ACI+event%3Apush .. image:: https://badge.fury.io/py/loopy.png :alt: Python Package Index Release Page :target: https://pypi.org/project/loopy/

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

pip install loopy

In addition, Loopy is compatible with and enhances

pyopencl 

Places on the web related to Loopy:

• Python package index 

  _ (download releases)

• Documentation 

  _ (read how things work)

• Github 

  _ (get latest source code, file bugs)

• Homepage 

  _