A novel shared memory framework for distributed deep learning in high-performance computing architecture

2018 ◽  
Author(s):  
Shinyoimg Ahn ◽  
Joongheon Kim ◽  
Sungwon Kang
Keyword(s):  
Deep Learning ◽  
High Performance Computing ◽  
Shared Memory ◽  
High Performance ◽  
Computing Architecture ◽  
Performance Computing

Hybridisation strategies and data structures for the NEMO ocean model

2017 ◽  
Vol 32 (6) ◽  
pp. 864-881 ◽  
Author(s):  
Italo Epicoco ◽  
Silvia Mocavero ◽  
Andrew R Porter ◽  
Stephen M Pickles ◽  
Mike Ashworth ◽  
...  
Keyword(s):  
High Performance Computing ◽  
Shared Memory ◽  
Data Structures ◽  
High Performance ◽  
Ocean Model ◽  
Data Layout ◽  
European Climate ◽  
Ocean Models ◽  
Performance Computing

This work describes the introduction of a second level of parallelism based on the OpenMP shared memory paradigm to NEMO, one of the most widely used ocean models in the European climate community. Although the existing parallelisation scheme in NEMO, based on the MPI paradigm, has served it well for many years, it is becoming unsuited to current high-performance computing architectures due to their increasing tendency to have fat nodes containing tens of compute cores. Three different parallel approaches for introducing OpenMP are presented, discussed and compared on several platforms. Finally we have also considered the effect on performance of the data layout employed in NEMO.

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