Accelerating Numerical Ice Engineering Tools Using GPGPU
- Shadi Alawneh (Oakland University) | Jan Willem Thijssen (C-CORE) | Martin Richard (C-CORE, Memorial University of Newfoundland)
- Document ID
- Offshore Technology Conference
- Arctic Technology Conference, 24-26 October, St. John's, Newfoundland and Labrador, Canada
- Publication Date
- Document Type
- Conference Paper
- 2016. Offshore Technology Conference
- 7 Management and Information, 7.2.3 Decision-making Processes, 7.2 Risk Management and Decision-Making, 7.2.1 Risk, Uncertainty and Risk Assessment, 1.6.9 Coring, Fishing, 6.3 Safety, 1.6 Drilling Operations
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C-CORE is engaged in understanding the iceberg and sea ice design loads needs of the energy sector. As the energy industry ventures into oceans with greater ice cover and more icebergs, there is a significant need for efficient engineering tools to plan and manage operations in exploration, production, and safety. Industry requires a range of scenarios for their risk assessments, where existing simulations can be computationally and time intensive.
C-CORE has recently started using the benefit of the General Purpose Computing on Graphical Processing Units (GPGPU) approach. This approach has shown significant speed up of several numerical ice engineering applications related to icebergs and sea ice. The investigated model types are Monte-Carlo type approaches for probabilistic design method, and quadratic discriminant. GPU computing with Compute Unified Device Architecture (CUDA) is a new approach to solve complex problems and transform the GPU into a massively parallel processor.
The present study applies the GPGPU technology to a Monte-Carlo simulation, used for a sea ice load application. The objective of this study is to measure the performance of the GPU using CUDA, and compare against the serial Central Processing Unit (CPU) using C++ and MATLAB implementations. Results show a speedup of up to 2,600 times of the GPGPU implementation compared to the MATLAB implementation, reducing the elapsed time from about 1.5 hour to about 2 seconds. This strongly indicates that the GPGPU approach can help the industry to significantly reduce the time required for the simulations.
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