Lecture (Other)

FZJ-2025-05564

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png GPU Programming Part 1: Foundations

Herten, A. (Corresponding author)FZJ* ; Meinke, J. (Corresponding author)FZJ* ; Haghighi Mood, K.FZJ* ; Penke, C.FZJ* ; Kraus, J.FZJ* ; Morgenstern, L.FZJ*

2025

Abstract: 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.Topics covered will include Introduction to GPUs and GPU computing, programming model CUDA, tools for debugging and profiling, GPU libraries (like cuBLAS, cuFFT), and introduction to multi-GPU programming.

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Contributing Institute(s):

1. Jülich Supercomputing Center (JSC)

Research Program(s):

1. 5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511) (POF4-511)
2. 5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511) (POF4-511)
3. 5122 - Future Computing & Big Data Systems (POF4-512) (POF4-512)
4. Grundlagen für zukünftige Informationstechnologien (FUEK412) (FUEK412)
5. ATML-X-DEV - ATML Accelerating Devices (ATML-X-DEV) (ATML-X-DEV)
6. BMBF 01 1H1 6013, NRW 325 – 8.03 – 133340 - SiVeGCS (DB001492) (DB001492)

Appears in the scientific report 2025

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