Accelerating Numerical Ice Engineering Tools Using GPGPU
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract 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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it