TY  - GEN
AU  - Garcia de Gonzalo, Simon
AU  - Oden, Lena
AU  - Herten, Andreas
AU  - Hrywniak, Markus
AU  - Kraus, Jiri
TI  - Efficient Distributed GPU Programming for Exascale
M1  - FZJ-2022-06173
PY  - 2022
AB  - Over the past years, GPUs became ubiquitous in HPC installations around the world. Today, they provide the majority of performance of some of the largest supercomputers (e.g. Summit, Sierra, JUWELS Booster). This trend continues in the pre-exascale and exascale systems (LUMI, Leonardo; Perlmutter, Frontier): GPUs are chosen as the core computing devices to enter this next era of HPC. To take advantage of future GPU-accelerated systems with tens of thousands of devices, application developers need to have the propers skills and tools to understand, manage, and optimize distributed GPU applications. In this tutorial, participants will learn techniques to efficiently program large-scale multi-GPU systems. While programming multiple GPUs with MPI is explained in detail, advanced tuning techniques and complementary programming models like NCCL and NVSHMEM are presented as well. Tools for analysis are shown and used to motivate and implement performance optimizations. The tutorial is a combination of lectures and hands-on exercises, using Europe's fastest supercomputer, JUWELS Booster with NVIDIA GPUs, for interactive learning and discovery.
T2  - ISC High Performance 2022
CY  - 29 May 2022 - 29 May 2022, Hamburg (Germany)
Y2  - 29 May 2022 - 29 May 2022
M2  - Hamburg, Germany
LB  - PUB:(DE-HGF)17
DO  - DOI:10.5281/ZENODO.6603470
UR  - https://juser.fz-juelich.de/record/916372
ER  -