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

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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>_