Lecture (Other)

FZJ-2022-05803

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png GPU Programming with CUDA

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

2022

Please use a persistent id in citations: http://hdl.handle.net/2128/33226

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 an NVIDIA GPU. The course covers basic aspects of GPU architectures and programming. Focus is on the usage of the parallel programming language CUDA C/C++ which allows maximum control of NVIDIA GPU hardware. Examples of increasing complexity are used to demonstrate optimization and tuning of scientific applications. Topics covered will include: Introduction to GPU/Parallel computing; Programming model CUDA; GPU libraries like CuBLAS and CuFFT; Tools for debugging and profiling; Performance optimizations; Advanced GPU programming model; CUDA Fortran in a nutshell.This course is a PRACE training course.

Note: Online course within the PRACE and FZJ training program.

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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. PRACE-6IP - PRACE 6th Implementation Phase Project (823767) (823767)

Appears in the scientific report 2022

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Database coverage:

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