TH‐A‐19A‐04: Latent Uncertainties and Performance of a GPU‐Implemented Pre‐Calculated Track Monte Carlo Method
Bibliographic record
Abstract
Purpose: Assessing the performance and uncertainty of a pre‐calculated Monte Carlo (PMC) algorithm for proton and electron transport running on graphics processing units (GPU). While PMC methods have been described in the past, an explicit quantification of the latent uncertainty arising from recycling a limited number of tracks in the pre‐generated track bank is missing from the literature. With a proper uncertainty analysis, an optimal pre‐generated track bank size can be selected for a desired dose calculation uncertainty. Methods: Particle tracks were pre‐generated for electrons and protons using EGSnrc and GEANT4, respectively. The PMC algorithm for track transport was implemented on the CUDA programming framework. GPU‐PMC dose distributions were compared to benchmark dose distributions simulated using general‐purpose MC codes in the same conditions. A latent uncertainty analysis was performed by comparing GPUPMC dose values to a “ground truth” benchmark while varying the track bank size and primary particle histories. Results: GPU‐PMC dose distributions and benchmark doses were within 1% of each other in voxels with dose greater than 50% of Dmax. In proton calculations, a submillimeter distance‐to‐agreement error was observed at the Bragg Peak. Latent uncertainty followed a Poisson distribution with the number of tracks per energy (TPE) and a track bank of 20,000 TPE produced a latent uncertainty of approximately 1%. Efficiency analysis showed a 937× and 508× gain over a single processor core running DOSXYZnrc for 16 MeV electrons in water and bone, respectively. Conclusion: The GPU‐PMC method can calculate dose distributions for electrons and protons to a statistical uncertainty below 1%. The track bank size necessary to achieve an optimal efficiency can be tuned based on the desired uncertainty. Coupled with a model to calculate dose contributions from uncharged particles, GPU‐PMC is a candidate for inverse planning of modulated electron radiotherapy and scanned proton beams. This work was supported in part by FRSQ‐MSSS (Grant No. 22090), NSERC RG (Grant No. 432290) and CIHR MOP (Grant No. MOP‐211360).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".