TY - JOUR
T1 - GeoDRIVE - A high performance computing flexible platform for seismic applications
AU - Kayum, Suha N.
AU - Tonellot, Thierry
AU - Etienne, Vincent
AU - Momin, Ali
AU - Sindi, Ghada
AU - Dmitriev, Maxim
AU - Salim, Hussain
N1 - KAUST Repository Item: Exported on 2022-06-21
Acknowledgements: The authors would like to acknowledge the support of Saudi Aramco's Emad Janoubi, Husain Shakhs, the work conducted as part of the Saudi Aramco/KAUST Exawave research collaboration and the collaboration with the King Fahad University of Petroleum and Minerals on FPGA. Additionally, the authors would like to acknowledge the support of the KAUST Supercomputing Laboratory and the usage of KAUST Shaheen II supercomputer for several of the runs presented.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.
PY - 2020/2/1
Y1 - 2020/2/1
N2 - GeoDRIVE, a high performance computing (HPC) software framework tailored to massive seismic applications and supercomputers is presented. The paper discusses the flexibility and modularity of the application along with optimized HPC features. GeoDRIVE's versatile design, associated to exascale computing capabilities, unlocks new classes of applications that significantly improve geoscientists' abilities to understand, locate and characterize challenging targets in complex settings. As a result, uncertainties in subsurface models are reduced both quantitatively and qualitatively, along with reduced drilling risks and improved prospect generation.
AB - GeoDRIVE, a high performance computing (HPC) software framework tailored to massive seismic applications and supercomputers is presented. The paper discusses the flexibility and modularity of the application along with optimized HPC features. GeoDRIVE's versatile design, associated to exascale computing capabilities, unlocks new classes of applications that significantly improve geoscientists' abilities to understand, locate and characterize challenging targets in complex settings. As a result, uncertainties in subsurface models are reduced both quantitatively and qualitatively, along with reduced drilling risks and improved prospect generation.
UR - http://hdl.handle.net/10754/679203
UR - https://www.earthdoc.org/content/journals/10.3997/1365-2397.fb2020015
UR - http://www.scopus.com/inward/record.url?scp=85096804347&partnerID=8YFLogxK
U2 - 10.3997/1365-2397.fb2020015
DO - 10.3997/1365-2397.fb2020015
M3 - Article
SN - 1365-2397
VL - 38
SP - 97
EP - 100
JO - First Break
JF - First Break
IS - 2
ER -