Histoptimizer¶
- “A heartbreaking work of monumental self-importance”
– My mom, probably
Histoptimizer takes an ordered list of item sizes and a number of partitions k, and returns a list of divider locations that partition the given list into k partitions with the lowest possible variance across the sums of the items in each partition.
Histoptimizer provides JIT- and SIMD-accelerated implementations on Intel and AMD processors, and a CUDA implementation for NVidia graphics cards. The implementations are in-core only and suitable for up to ~ 1 million items with 32-bit floating point sizes.
Histoptimizer provides NumPY and Pandas APIs, and a CLI that supports CSVs and Pandas JSON. It comes with everything you could want, except a valid use case.
Installation¶
To get started with Histoptimizer, install the latest stable release via pip:
pip install histoptimizer
Histoptimizer currently supports Python 3.8+ and relies on the following fine software projects:
Releases¶
Releases are listed at https://github.com/delusionary/histoptimizer/releases/
License¶
Histoptimizer is licensed under the 0BSD license. See the LICENSE for more information.
About¶
Histoptimizer started as a simple exercise for learning CUDA programming and wound up as an exercise for brushing up on modern Python module development tools and best practices. At no point in between did it make contact with a practical application or use case that could not be approximated more quickly and easily by other methods.
There may be practical applications; if so, please don’t tell me. There’s a certain platonic purity to it right now. Freeing, if you think about it. It does nothing of value, but does it very quickly, so as to make it up in volume.
About the Author¶
The author loves buckets and owns a variety of them, even one of those Yeti ones, and makes no apologies for it.
