An efficient C++17 GPU numerical computing library with Python-like syntax
-
Updated
May 28, 2024 - C++
An efficient C++17 GPU numerical computing library with Python-like syntax
Stretching GPU performance for GEMMs and tensor contractions.
A hardware-accelerated GPU terminal emulator focusing to run in desktops and browsers.
CUDA C++ Core Libraries
The fastest and most memory efficient lattice Boltzmann CFD software, running on all GPUs via OpenCL.
A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.
A fast, ergonomic and portable tensor library in Nim with a deep learning focus for CPU, GPU and embedded devices via OpenMP, Cuda and OpenCL backends
Numerical linear algebra software package
Pythonic particle-based (super-droplet) warm-rain/aqueous-chemistry cloud microphysics package with box, parcel & 1D/2D prescribed-flow examples in Python, Julia and Matlab
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.
Fortran interface for DLA-Future
Suite of python packages for multiparticle simulations of particle accelerators.
Implementation of SYCL and C++ standard parallelism for CPUs and GPUs from all vendors: The independent, community-driven compiler for C++-based heterogeneous programming models. Lets applications adapt themselves to all the hardware in the system - even at runtime!
Massively parallel Monte Carlo diffusion MR simulator written in Python.
TornadoVM: A practical and efficient heterogeneous programming framework for managed languages
Video stabilization using gyroscope data
hipBLASLt is a library that provides general matrix-matrix operations with a flexible API and extends functionalities beyond a traditional BLAS library
Add a description, image, and links to the gpu-computing topic page so that developers can more easily learn about it.
To associate your repository with the gpu-computing topic, visit your repo's landing page and select "manage topics."