Lecture (Other)

FZJ-2023-05176

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* ; Kraus, J.FZJ* ; Hrywniak, M.FZJ*

2023

This record in other databases:

Please use a persistent id in citations: doi:10.34734/FZJ-2023-05176

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 covers 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.For the first time, the GPU Programming with CUDA course is held in two parts. 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.An advanced course with more involved and specific topics is available as an individual entry.

---

Contributing Institute(s):

1. Jülich Supercomputing Center (JSC)

Research Program(s):

1. 5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511) (POF4-511)
2. 5122 - Future Computing & Big Data Systems (POF4-512) (POF4-512)
3. 5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511) (POF4-511)
4. ATML-X-DEV - ATML Accelerating Devices (ATML-X-DEV) (ATML-X-DEV)

Appears in the scientific report 2023

---

Database coverage:

---