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This is the code for "An introduction to GPU Programming with CUDA" by Siraj Raval on Youtube

Public Repository to be used in Coursera Course titled "Introduction to Concurrent Programming with GPUs"

Intro Parallel Programming for GPUs with CUDA

A very basic and gentle intro to Triton, an open-source language for GPU programming supported by OpenAI.

Introduction to GPU programming

Presentation

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Introduction to GPU programming

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Introduction to GPU Programming Support Code

Intro to GPU Programming Course

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Quizzes Solution and explanation

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Coursera notes and documentation of the assignments to Introduction to Concurrent Programming with GPUs. This repository is being provided solely for informational purposes. I do not warrant the quality, accuracy or completeness of any information provided and such information is provided as is without any warranty or condition of any kind.

Code and labs for the Introduction to Concurrent Programming with GPUs course offered by Johns Hopkins University

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Intro to GPU Programming Project Dr. Ron Coleman

this work shop held by CalculQuebec

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CUDA by Example: An Introduction to General-Purpose GPU Programming

IPython notebooks for mini/intro talk on gpu programming with CUDA

Intro to CUDA Tile based GPU Python Programming with uv package manager

C++/CUDA repository as an intro to GPU computing and parallel programming

Intro to Parallel Programming CUDA Udacity by Nvidia - Using CUDA to Harness the Power of GPUs

This is the final project of Mingrui's "Intro to GPU Programming". We accelerated the inference delay on CUDA to 114 ms.

Intro to Machine Learning - TensorFlow Project Project code for Udacity's Intro to Machine Learning with TensorFlow Nanodegree program. In this project, you will first develop code for an image classifier built with TensorFlow, then you will convert it into a command line application. In order to complete this project, you will need to use the GPU enabled workspaces within the classroom. The files are all available here for your convenience, but running on your local CPU will likely not work well. You should also only enable the GPU when you need it. If you are not using the GPU, please disable it so you do not run out of time! Data The data for this project is quite large - in fact, it is so large you cannot upload it onto Github. If you would like the data for this project, you will want download it from the workspace in the classroom. Though actually completing the project is likely not possible on your local unless you have a GPU. You will be training using 102 different types of flowers, where there ~20 images per flower to train on. Then you will use your trained classifier to see if you can predict the type for new images of the flowers.