A small library for gpu computing
-
Updated
Aug 1, 2024 - C
A small library for gpu computing
Tools : CUDA C, Multicore Programming, Batch Scripting, MATLAB
A comprehensive collection of projects developed for the CS426 - Parallel Computing course at Bilkent University. This repository showcases implementations of various parallel computing techniques and algorithms, highlighting the use of MPI, OMP, CUDA and GPU programming.
A graphics application that shows a ray-tracing scene rendered by the fragment shader on a frame buffer object.
Simple SYCL examples
Cocaine is a multi-platform C library that can be used to accelerate large workloads/big data/anything really with the power of a GPU with ease. A .NET wrapper is available in the link below.
OpenCL interop rendering abstractions that simulate the OpenGL pipeline
Distributed and serial implementations of the 2D Convolution operation in c++ and CUDA.
General processing using GPU through compute shaders
Comprarison of vector operation using CPU vs GPU using Nvidia Cuda
Learn OpenCL step by step.
Programming exercises on parallel computing using OpenMP, OpenMPI and CUDA.
Materials for "Differences between OpenACC and OpenMP offloading models" tutorial.
Solutions of design exercises in CS433A: Parallel Programming, Spring Semester 2021-22
Parallelized version of Counting Sort using CUDA
Objective: To find the bottlenecks in the unoptimized single-threaded program for Checkered Matrix Multiplication using PMCs through perf and implemented the optimized single-threaded, multi-threaded and GPU program using CUDA for the same.
Add a description, image, and links to the gpu-programming topic page so that developers can more easily learn about it.
To associate your repository with the gpu-programming topic, visit your repo's landing page and select "manage topics."