CUDA training

Intro

Prerequisites

prerequisites

Table of contents

Reference

Who is the course for?

This course is for students, researchers, engineers and programmers who would like to learn GPU programming with CUDA. Some previous experience with C/C++ is required, no prior knowledge of CUDA is needed.

Tentative schedule

Day 1	Thursday, October 7, 2021
9:00 - 9:10	Welcome and introduction to the training course
9:10 - 9:30	Introduction to GPUs
9:30 - 10:10	Using CUDA
10:10 - 10:20	Break
10:20 - 10:50	Adding two vectors on the GPU
10:50 - 11:10	Break-out rooms
11:10 - 11:20	Break
11:20 - 12:30	Solving heat equation with CUDA
12:30 - 12:50	Break-out rooms
12:50 - 13:00	Wrap-up

Day 2	Friday, October 8, 2021
9:00 - 9:10	Follow-ups from day 1
9:10 - 10:10	Optimizing the CUDA kernel
10:10 - 10:30	Break-out rooms
10:30 - 10:50	Optimizing the CUDA kernel (cont.)
10:50 - 11:00	Break
11:00 - 11:50	Exploring task-based parallelism
11:50 - 12:10	Break-out rooms
12:10 - 12:50	Exploring task-based parallelism (cont.)
12:50 - 13:00	Wrap-up

About the course

These course materials are developed for those who wants to leark GPU programming with CUDA from the beginning. The course consists of lectures, type-along and hands-on sessions.

During the first day, we will cover the architecture of the GPU accelerators, basic usage of CUDA, and how to control data movement between CPUs and GPUs. The second day focuses on more advanced topics, such as how to optimize computational kernels for efficient execution on GPU hardware and how to explore the task-based parallelism using streams and events. We will also briefly go through profiling tools that can help one to identify the computational bottleneck of the applications.

After the course the participants should have the basic skills needed for using CUDA in new or existing applications.

The participants are assumed to have knowledge of C programming language. Since participants will be using HPC clusters to run the examples, fluent operation in a Linux/Unix environment is assumed.

Credits

The lesson file structure and browsing layout is inspired by and derived from work by CodeRefinery licensed under the MIT license. We have copied and adapted most of their license text.

Instructional Material

This instructional material is made available under the Creative Commons Attribution license (CC-BY-4.0). The following is a human-readable summary of (and not a substitute for) the full legal text of the CC-BY-4.0 license. You are free to:

share - copy and redistribute the material in any medium or format
adapt - remix, transform, and build upon the material for any purpose, even commercially.

The licensor cannot revoke these freedoms as long as you follow these license terms:

Attribution - You must give appropriate credit (mentioning that your work is derived from work that is Copyright (c) Artem Zhmurov and individual contributors and, where practical, linking to https://enccs.github.io/CUDA), provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
No additional restrictions - You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.

With the understanding that:

You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation.
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.

Software

Except where otherwise noted, the example programs and other software provided with this repository are made available under the OSI-approved MIT license.