cuda-c

There are 11 repositories under cuda-c topic.

  • russellmatt66/imhd-CUDA

    CUDA C implementation of the Lax-Wendroff scheme for solving the Ideal MHD equations in 3D

    Language:Cuda7102

  • tedliosu/cuda_mergesort_ytl

    My personal attempt at creating a relatively fast iterative mergesort that runs on CUDA and HIP GPUs

    Language:Cuda6100

  • MatteoOrlandini/Embedded-Systems-Exam

    Implementation of One Sided Jacobi SVD using CUDA on Jetson TK1 embedded GPU

    Language:Cuda5102

  • GPUEngineering/GPUtils

    A C++ header-only library for parallel linear algebra on GPUs (CUDA/cuBLAS under the hood)

    Language:Cuda41300

  • mahmoud-joumaa/CSC447_Assignment3

    Parallelizing matrix multiplication using Cuda C. Tiling is also implemented to compare results. This repository is submitted as the third assignment for the CSC447 (Parallel Programming for Multicore and Cluster Systems) course at the Lebanese American University.

    Language:Jupyter Notebook110

  • mahmoud-joumaa/CSC447_FinalProject

    This team project is presented as the final project for the CSC447 (Parallel Programming for Multicore and Cluster Systems) course at the Lebanese American University under the supervision of Dr. Hamdan Abdellatef.

    Language:C1100

  • AgemennoN/CPU-GPU_Benchmarking

    CPU-GPU Benchmarking

    0100

  • Aynur19/CUDA-XP

    Learning CUDA Programming

    Language:C++0101

  • mDp0r/pacman4console

    Reinforcement learning console based PacMan Game

    Language:C0001

  • eduardosantoshf/cuda-c

    CLE Third Assignment - The objective of this project was to take the second general problem, which have been discussed in the lab classes and for which we have developed both a multithreaded and a multiprocess solution. The aim now was to convert it into a CUDA program to be ran in a GPU under Linux.

    Language:Cuda10

  • Ferdib-Al-Islam/gpu_parallelization

    Co-occurrence matrices act as the input to many unsupervised learning algorithms, including those that learn word embedding, and modern spectral topic models. However, the computation of these inputs often takes longer time than the inference. While much thought has been given to implementing fast learning algorithms. The co-occurrence matrix computation tasks are well suited to GPU parallelization. GPUs or other specialized hardware, have never been used to explicitly compute word-to-word co-occurrence matrix.

    Language:Jupyter Notebook202