Learn CUDA with Docker | No NVIDIA GPUs required!
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How to code with CUDA, but without a GPU! -
Basic knowladge about CUDA programming -
Ability to desing and implement CUDA parallel algorithms
- Basic C or C++ programming knowledge
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!
WHAT DO YOU LEARN?
We will demonstrate how you can learn CUDA with the simple use of: Docker: 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
WHAT’S NEW?
LIVE CLASS SERIES 2020!
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.
INTERACTIVE PLAYGROUNDS
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.
WHY CUDA?
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.
DISCLAIMER
Some of the images used in this course are copyrighted to NVIDIA.
- Any one who wants to learn CUDA programming, but does NOT have access to expensive GPUs
