Parallelized JUNO simulation software based on SNiPER
Bibliographic record
Abstract
The Jiangmen Underground Neutrino Observatory (JUNO) is a neutrino experiment to determine neutrino mass hierarchy. It has a central detector used for neutrino detection, which consists of a spherical acrylic vessel containing 20 kt LS and about 18,000 20-inch PMTs to collect light from LS. Around the CD, there is a water pool to shield radioactivities. The WP is equipped with about 2000 PMTs to measure cosmic ray muons by detecting Cherenkov light. As one of the important parts in JUNO offline software, the serial simulation framework is developed based on SNiPER. It is in charge of physics generator, detector simulation, event mixing and digitization. However Geant4 based detector simulation of such a large detector is time-consuming and challenging. It is necessary to take full advantages of parallel computing to speedup simulation. Starting from version 10, Geant4 supports event-level parallelism. Even though based on pthread, it could be extended with other libraries such as Intel TBB. Therefore it is possible to parallelize JUNO simulation framework via integrating Geant4 and SNiPER. In this paper, our progress in developing parallelized simulation software are presented. The SNiPER framework can run in sequential mode, Intel TBB mode or other modes. The SNiPER task component is in charge of event loop, which is like a simplified application manager. Two types of tasks are introduced in the simulation framework, one is global task and another is worker task. The global task will run only once to initialize detector geometry and physics processes. The worker tasks will be spawned after global task is done. In each worker task, a Geant4 run manager is invoked to do the real simulation. Therefore the simulation framework and the underlying TBB have been decoupled. Finally, the software performance of parallelized JUNO simulation software is also presented.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".