Galactic and Gravitational Dynamics in Python (+ GPU and autodiff)
-
Updated
May 31, 2024 - Python
Galactic and Gravitational Dynamics in Python (+ GPU and autodiff)
The primary source code repository for PHCpack, a software package to solve polynomial systems with homotopy continuation methods.
CUDA C++ Core Libraries
A project to facilitate construction of high-performance simulations of graph-structured systems.
(REOS) Radar and ElectroOptical Simulation Framework written in Fortran.
A model-independent chemistry module for atmosphere models
SLATE is a distributed, GPU-accelerated, dense linear algebra library targetting current and upcoming high-performance computing (HPC) systems. It is developed as part of the U.S. Department of Energy Exascale Computing Project (ECP).
A library of GPU-enabled data processing and reconstruction methods for tomography
A hardware-accelerated GPU terminal emulator focusing to run in desktops and browsers.
Distributed Communication-Optimal Matrix-Matrix Multiplication Algorithm
Safe rust wrapper around CUDA toolkit
YUP is an open-source library dedicated to empowering developers with advanced tools for cross-platform application development.
Stretching GPU performance for GEMMs and tensor contractions.
Efficient streaming of sparse event data supporting files, network I/O, GPU peripherals (via Torch/Jax/Numpy) and neuromorphic protocols
(REOS) Radar and Electro-Optical Simulation Framework written in C++.
OpenCL is the most powerful programming language ever created. Yet the OpenCL C++ bindings are cumbersome and the code overhead prevents many people from getting started. I created this lightweight OpenCL-Wrapper to greatly simplify OpenCL software development with C++ while keeping functionality and performance.
A WebGL accelerated JavaScript library for training and deploying ML models.
CHAI and RAJA provide an excellent base on which to build portable codes. CARE expands that functionality, adding new features such as loop fusion capability and a portable interface for many numerical algorithms. It provides all the basics for anyone wanting to write portable code.
Run GPU inference and training jobs on serverless infrastructure that scales with you.
Add a description, image, and links to the gpu-acceleration topic page so that developers can more easily learn about it.
To associate your repository with the gpu-acceleration topic, visit your repo's landing page and select "manage topics."