Lecture (Other)

FZJ-2021-05271

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

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

2021

Please use a persistent id in citations: http://hdl.handle.net/2128/29491

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 will cover basic 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.Topics covered include: Introduction to GPU/Parallel computing Programming model CUDA GPU libraries like CuBLAS and CuFFT Tools for debugging and profiling Performance optimizationsThis 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. 5121 - Supercomputing & Big Data Facilities (POF4-512) (POF4-512)
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 2021

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Database coverage:

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