Course Information
Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
2 Hour(s) 58 Minute(s)
Language
English
Rating
Course Overview
Learrn to Code with CUDA with GPGPU-Simulators & Docker, Kickstart Your Computing and Data Science Career!
WELCOME!
We present you the long waited approach to Learn CUDA WITHOUT NVIDIA GPUS! Finally, you can learn CUDA just on your laptop, tablet or even on your mobile, and that's it! CUDA provides a general-purpose programming model which gives you access to the tremendous computational power of modern GPUs, as well as powerful libraries for machine learning, image processing, linear algebra, and parallel algorithms.
WHAT DO YOU LEARN?
We will demonstrate how you can learn CUDA with the simple use of Docker and OS-level virtualization to deliver software in packages called containers and GPGPU-Sim, a cycle-level simulator modeling contemporary graphics processing units (GPUs) running GPU computing workloads written in CUDA or OpenCL. This course aims to introduce you with the NVIDIA's CUDA parallel architecture and programming model in an easy-to-understand way. We plan to update the lessons and add more lessons and exercises every month!
Virtualization basics
Docker Essentials
GPU Basics
CUDA Installation
CUDA Toolkit
CUDA Threads and Blocks in various combinations
CUDA Coding Examples
Based on your earlier feedback, we are introducing a Zoom live class lecture series on this course through which we will explain different aspects of the Parallel and distributed computing and the High Performance Computing (HPC) systems software stack: Slurm, PBS Pro, OpenMP, MPI and CUDA! Live classes will be delivered through the Scientific Programming School, which is an interactive and advanced e-learning platform for learning scientific coding. Students purchasing this course will receive free access to the interactive version (with Scientific code playgrounds) of this course from the Scientific Programming School (SCIENTIFIC PROGRAMMING IO) . Instructions to join are given in the bonus content section.
DISCLAIMER
Some of the images used in this course are copyrighted to NVIDIA.
Course Content
- 10 section(s)
- 44 lecture(s)
- Section 1 Introduction
- Section 2 CUDA foundation
- Section 3 CUDA threads, blocks and grid
- Section 4 CUDA memory models
- Section 5 CUDA vector addition
- Section 6 CUDA matrix multiplication
- Section 7 CUDA streams
- Section 8 NVIDIA Docker Container Toolkit
- Section 9 CUDA for Dummies
- Section 10 Additonal Contents
What You’ll Learn
- How to code with CUDA, but without a GPU!, Basic knowladge about CUDA programming, Ability to desing and implement CUDA parallel algorithms
Reviews
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SStephen White
The instructor that gave most of the lectures was hard to understand sometimes due to foreign accent.
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JJoseph Quinn
The "bonus" lectures for this course were far more instructive and better presented than the content initially given by the course. A lack of running code to show what proper output should look like also hampers the learning process. Finally, there is nothing really taught about using the Docker container properly.
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RRaul Ramirez-Velarde
Excellent course. Builds knowledge paced but concise. I found other course much harder to follow. I happy I chose this one
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MMax
the course is OK, it helps to build a better intuition behind the GPU technology. Few things could be done better: * there are no practical tasks at all. That's crucial for learning new topics in-depth. * no quizzes either. * some details (in lessons) are not well thought. (good in general, but quite a room for improvement)
