Future-proof parallelism for electron-atom scattering codes with PRMAT
Bibliographic record
Abstract
Electron collisions with atoms were among the earliest problems studied using quantum mechanics.However, the accurate computation of much of the data required in astrophysics and plasma physics still presents huge computational challenges, even on the latest generation of high-performance computer architectures, such as the Cray XT series.In recent years a suite of parallel programs based on the 'R-matrix' ab initio approach to variational solution of the many-electron Schrodinger equation has been developed and has enabled much accurate scattering data to be produced.However, future calculations will require substantial increases in both the numbers of channels and scattering energies involved in the R-matrix propagations.This paper describes how many of these computational challenges have been addressed in two ways: by substantially improving the parallel performance of the PFARM 'external region' code on HECToR, and by developing a new Airy Logarithmic Derivative propagator code, FARM2, that is much more memory efficient while maintaining accuracy and performance, and which is (deliberately) much more speculative and 'futuristic' in its experimentation with Fortran 2003 and MPI-2 features.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".