Computing with Python functions.
-
Updated
Apr 30, 2025 - Python
Computing with Python functions.
A package which efficiently applies any function to a pandas dataframe or series in the fastest available manner
Evolutionary algorithm toolbox and framework with high performance for Python
Concurrent Python made simple
📈 Adaptive: parallel active learning of mathematical functions
OpenCL integration for Python, plus shiny features
Python bindings for MPI
Numba extension for compiling Pandas data frames, Intel® Scalable Dataframe Compiler
An optimization and data collection toolbox for convenient and fast prototyping of computationally expensive models.
Zero-copy MPI communication of JAX arrays, for turbo-charged HPC applications in Python ⚡
Parallel programming with Python
Lightweight fast function pipeline (DAG) creation in pure Python for scientific workflows 🕸️🧪
A suite of benchmarks for CPU and GPU performance of the most popular high-performance libraries for Python 🚀
Comfortable parallel TQDM using concurrent.futures
PyTorch implementation of Soft-Actor-Critic and Prioritized Experience Replay (PER) + Emphasizing Recent Experience (ERE) + Munchausen RL + D2RL and parallel Environments.
optimagic is a Python package for numerical optimization. It is a unified interface to optimizers from SciPy, NlOpt and other packages. optimagic's minimize function works just like SciPy's, so you don't have to adjust your code. You simply get more optimizers for free. On top you get diagnostic tools, parallel numerical derivatives and more.
A fast poisson image editing implementation that can utilize multi-core CPU or GPU to handle a high-resolution image input.
Distributed and Parallel Computing Framework with / for Python
https://wavespeed.ai/ Context parallel attention that accelerates DiT model inference with dynamic caching
High-Performance Python Compute Engine for Data and AI
Add a description, image, and links to the parallel-computing topic page so that developers can more easily learn about it.
To associate your repository with the parallel-computing topic, visit your repo's landing page and select "manage topics."