TH‐D‐BRD‐02: Convolution‐Superposition Dose Calculations with GPUs
Bibliographic record
Abstract
Purpose: To study the impact in terms of execution time and accuracy of using graphics hardware for calculating the dose in a treatment planning system. The architecture of Graphics Processing Units (GPU) is well suited for numerical tasks that are intrinsically parallel, such as dose calculations. Method and Materials: This work was made within the framework of PlanUNC, or PLUNC, a treatment planning system developed and maintained by the Department of Radiation Oncology of the University of North Carolina at Chapel Hill for research and development purposes. The objective was to transparently integrate a GPU dose calculation engine to PLUNC. The CUDA platform from NVIDIA was used for the GPU implementation. A convolution/superposition (CS) dose calculation algorithm was ported by developing programs (called kernels) that are executed on the GPU. Firstly, the CS engine of PLUNC was directly ported to the GPU, with the original code preserved as much as possible. Secondly, parts of the original algorithm were redesigned to better exploit the massively parallel architecture of GPUs. The numerical experiments were conducted with a NVIDIA GeForce GTX280 and an Intel Q6600 CPU. Results: Acceleration factors of 10× to 20× were achieved with the GPU implementation relative to the CPU version with the direct port of the CS algorithm. The numerical accuracy of the results was preserved with the GPU implementation. A 40× acceleration factor was obtained for the TERMA calculation subroutine, which was rewritten with the GPU architecture in mind. These acceleration factors were sufficient to significantly improve the responsiveness of the PLUNC graphical interface. Conclusion: This work demonstrates the potential of graphics hardware for dose calculation in treatment planning systems. This could in turn have a significant impact on optimization strategies for complex delivery techniques such as IMRT. Research sponsored by Varian Medical Systems, Inc.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".