Lecture (Other)

FZJ-2022-05791

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Directive-based GPU programming with OpenACC

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

2022

Please use a persistent id in citations: http://hdl.handle.net/2128/33086

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 the GPU. The course covered basic aspects of GPU architectures and programming. Focus was on the usage of the directive-based OpenACC programming model, which allows for portable application development. Examples of increasing complexity were used to demonstrate optimization and tuning of scientific applications. Topics covered: Introduction to GPU/Parallel computing, Programming model OpenACC, Interoperability of OpenACC with GPU libraries (like cuBLAS and cuFFT) and CUDA, Multi-GPU Programming with MPI and OpenACC, Tools for debugging and profiling, Performance optimization. The course consists of lectures and interactive hands-on sessions in C or Fortran (the attendee’s choice).

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Contributing Institute(s):

1. Jülich Supercomputing Center (JSC)

Research Program(s):

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

Appears in the scientific report 2022

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Database coverage:

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