Intermediate OpenACC/CUDA

Prerequisites

A cloud environment will be used for this workshop which requires authentication through GitHub. Please go to https://github.com/ and apply for an account if needed.

Alternatively, you can use an HPC cluster that you have access to provided that it has Nvidia GPUs and the PGI compiler installed. Alternatively, you can use Google Colab.

Instructions can be found in the Setup section.

Table of contents

• Introduction to GPU
• OpenACC: Analysis and Parallelization
• OpenACC: Optimization
• CUDA: Optimizing the GPU kernel
• CUDA: Asynchronous execution

Reference

• Setup
• Quick Reference
• Instructor’s guide

Who is the course for?

This course is for students, researchers, engineers and programmers who would like to expand their knowledge of OpenACC and CUDA. Some previous experience with C/C++ is required, basic knowledge of OpenACC/CUDA will help to follow the material.

The lesson furthermore assumes that participants have some familiarity with the following topics:

• Logging in to supercomputers and using a bash terminal

• Compiling C/C++ or Fortran codes using compilers and makefiles

If you are new to OpenACC and/or CUDA

We encourage people without prior experience of OpenACC and/or CUDA to go through our introductory lesson and work on the exercises before attending this workshop. The materials are avaliable online at https://enccs.github.io/OpenACC-CUDA-beginners/.

Tentative schedule

Day 1
9:00 - 9:10	Introduction to ENCCS
9:10 - 9:30	Introduction to GPUs
9:30 - 9:50	OpenACC: Analysis and Parallelization
9:50 - 10:20	Break-out rooms
10:20 - 10:30	Break
10:30 - 10:50	OpenACC: Optimization
10:50 - 11:20	Break-out rooms
11:20 - 11:30	Break
11:30 - 12:30	CUDA: Parallel reduction use-case

Day 2
9:00 - 9:10	Follow-ups from day 1
9:10 - 10:00	CUDA: Optimizing the reduction kernel
10:00 - 10:20	Break-out rooms
10:20 - 10:30	Break
10:30 - 11:20	CUDA: Exploring task-based parallelizm: streams and events
11:20 - 11:40	Break-out rooms
11:40 - 11:50	Break
11:50 - 12:20	CUDA: Notes on profiling tools
12:20 - 12:30	Wrap-up

About the course

These course materials are developed for those who have the understanding of fundamentals of OpenACC and CUDA and would like to expand their knowledge. The course consists of lectures, type-along and hands-on sessions.

The lectures will present the OpenACC framework with three key steps namely analysis, parallelization, and Optimization, in porting to high performance accelerated codes. CUDA lectures cover two main topics: how to optimize computational kernels for effitient execution on GPU hardware and how to explore the task-based parallelizm using streams and events. We will also briefly go through profiling tools that can help one to identify the computational bottleneck of the programm.

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

See also

• Our introductory materials

• Official OpenACC site

• Nvidia Deep Learning institute

• CSC training resources on OpenACC

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) ENCCS and individual contributors and, where practical, linking to https://enccs.github.io/sphinx-lesson-template), 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.