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@MISC{Herten:915928,
author = {Herten, Andreas and Hater, Thorsten and Haghighi Mood,
Kaveh and Kraus, Jiri and Hrywniak, Markus},
title = {{D}irective-based {GPU} programming with {O}pen{ACC}},
reportid = {FZJ-2022-05791},
year = {2022},
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).},
month = {Oct},
date = {2022-10-26},
organization = {PRACE Training Course at JSC, online
(Germany), 26 Oct 2022 - 28 Oct 2022},
subtyp = {Other},
cin = {JSC},
cid = {I:(DE-Juel1)JSC-20090406},
pnm = {5122 - Future Computing $\&$ Big Data Systems (POF4-512) /
5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
(SDLs) and Research Groups (POF4-511) / ATML-X-DEV - ATML
Accelerating Devices (ATML-X-DEV)},
pid = {G:(DE-HGF)POF4-5122 / G:(DE-HGF)POF4-5111 /
G:(DE-Juel-1)ATML-X-DEV},
typ = {PUB:(DE-HGF)17},
url = {https://juser.fz-juelich.de/record/915928},
}