GPU Programming Part 1: Foundations

8 Apr 2024, 09:00 → 10 Apr 2024, 16:30 Europe/Berlin

Bldg. 16.3. Rm 213a (Ausbildungsraum 1) (Jülich Supercomputing Centre)

Andreas Herten (Forschungszentrum Jülich), Jan Meinke (Forschungszentrum Jülich)

GPU-accelerated computing drives current scientific research. Writing fast numeric algorithms for GPUs offers high application performance by offloading compute-intensive portions of the code to a GPU.

The course will cover aspects of GPU architectures and programming. Focus is on the usage of the parallel programming language CUDA C++, which allows maximum control of NVIDIA GPU hardware. Examples of increasing complexity are used to demonstrate optimization and tuning of scientific applications.

This course is a basic course covering the foundations of GPU programming including an introduction to GPU/parallel computing, programming with CUDA, GPU libraries, tools for debugging and profiling, and performance optimizations.

In addition, an advanced course is available with modules providing more in-depth coverage of multi-GPU programming, modern CUDA concepts, CUDA Fortran, and portable programming models such as OpenACC and C++ parallel STL algorithms. The advanced modules will be taught from 3-7 June 2024, see the dedicated announcement for registration.

 

Topics covered will include

• Introduction to GPUs and GPU Computing
• Programming Model CUDA
• Tools for Debugging and Profiling
• GPU Libraries (like cuBLAS, cuFFT)
• Introduction to Multi-GPU Programming

 

Prerequisites: Some knowledge about Linux, e.g. make, command line editor, Linux shell, experience in C/C++

 

Dates:
8-10 April 2024, 09:00-16:30 each day

 

Location: Jülich Supercomputing Centre, building 16.3, room 213a (Ausbildungsraum 1)

 

Language: This course is given in English.

 

Instructors: Dr. Jan Meinke, Dr. Kaveh Haghighi-Mood, Dr. Andreas Herten, JSC; Jiri Kraus, Markus Hrywniak, NVIDIA
 

Contact 
For any questions concerning the course, please send an e-mail to j.meinke@fz-juelich.de

Monday, 8 April

1 Introduction

10:30 Coffee break

2 Introduction

12:15 Lunch break

3 Introduction

14:45 Coffee break

4 Introduction

Tuesday, 9 April

5 Tools

10:30 Coffee break

6 Tools

12:15 Lunch break

7 Matrix Multiplication

14:45 Coffee break

8 Performance Optimization

Wednesday, 10 April

9 CUDA Streams

10:30 Coffee break

10 CUDA Streams

12:15 Lunch break

11 Multi-GPU Programming

14:45 Coffee break

12 Multi-GPU Programming