A library of GPU-enabled data processing and reconstruction methods for tomography
-
Updated
May 31, 2024 - Python
A library of GPU-enabled data processing and reconstruction methods for tomography
Galactic and Gravitational Dynamics in Python (+ GPU and autodiff)
Stretching GPU performance for GEMMs and tensor contractions.
GPU Accelerated Euclidean Distance Transform
⚡️Optimizing einsum functions in NumPy, Tensorflow, Dask, and more with contraction order optimization.
Distributed GPU-Accelerated Framework for Evolutionary Computation. Comprehensive Library of Evolutionary Algorithms & Benchmark Problems.
Generative AI reference workflows optimized for accelerated infrastructure and microservice architecture.
High-throughput tomography pipeline
Solvers for NP-hard and NP-complete problems with an emphasis on high-performance GPU computing.
hardware-accelerated array language interpreter
Efficient Permutation-based GWAS for Normal and Skewed Phenotypic Distributions
A highly efficient implementation of Gaussian Processes in PyTorch
This project is a deep learning model built with PyTorch that classifies images into two categories: cats and dogs.
Machine learning library for symbolic fitting: the unknown system/function is described via NARMAX algebraic expressions being linear combinations of arbitrary non-linear terms provided by the user (like 0.2x²+0.7sin(x) or x[k-1]*y[k-4]^2).
Create IFS fractals on the GPU!
NVIDIA Merlin is an open source library providing end-to-end GPU-accelerated recommender systems, from feature engineering and preprocessing to training deep learning models and running inference in production.
This repo contains code that implements vPET-ABC. Currently, we have included Python code attempting GPU acceleration on FDG compartment models.
LLM-Based Quantitative Trading Python Library
A brian2 extension to simulate spiking neural networks on GPUs
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."