Sci-Thur AM: Planning - 11: The impact of distributed calculation framework settings on plan calculation time
Bibliographic record
Abstract
Some treatment planning system can divide a treatment plan calculation into multiple threads and allow both local and network computing resources to perform the calculation concurrently, which significantly reduces the calculation time for a calculation-demanding planning such as Volumetric Modulated Arc Therapy (VMAT) or electron Monte Carlo (eMC). This study tested in Eclipse (Varian, V10.0.39) the impact of Distributed Calculation Framework (DCF, V10.0.0.757) settings on calculation time in a planning environment that consists of 20 workstations with 8 core processors and 16GB RAMs installed on most of them. It is found that for an arc plan increasing the control point field parallelization factor reduces the total calculation time at beginning but lengthens the total calculation time after a certain level as a result of data sending time increase. Further increasing the factor may cause a serious net work traffic or even failure of a calculation. For an eMC plan the calculation time decreases monotonously with the increase of Monte carlo field parallelization factor, and the data sending time is insignificant compared to the calculation time. Increasing the local servant numbers reduces the data sending time but raises the calculation time for arc and eMC plans. The calculation time increment is more and more significant with the increase of local servants. The optimal DCF setting for a facility depends on the total number of calculation workstations available, the hardware configuration of the workstations, and the data transfer rate of the network. No conflict of interest exists in the study.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".