Lecture (Other)

FZJ-2023-05224

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png GPU Programming Part 2: Advanced GPU Programming

Meinke, J.FZJ* ; Herten, A. (Corresponding author)FZJ* ; Hrywniak, M.FZJ* ; Kraus, J.FZJ* ; Haghighi Mood, K.FZJ*

2023

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Please use a persistent id in citations: doi:10.34734/FZJ-2023-05224

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. The foundations of GPU programming are covered in another dedicated Basic Course.This advanced course consists of 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.

Note: See https://juser.fz-juelich.de/record/1019123 for the JuSER entry for the Basic Course.

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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. 5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511) (POF4-511)
3. 5122 - Future Computing & Big Data Systems (POF4-512) (POF4-512)
4. ATML-X-DEV - ATML Accelerating Devices (ATML-X-DEV) (ATML-X-DEV)

Appears in the scientific report 2023

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Database coverage:

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