Custom math functions for molecular dynamics
Bibliographic record
Abstract
While developing the protein folding application for the IBM Blue Gene®/L supercomputer, some frequently executed computational kernels were encountered. These were significantly more complex than the linear algebra kernels that are normally provided as tuned libraries with modern machines. Using regular library functions for these would have resulted in an application that exploited only 5–10% of the potential floating-point throughput of the machine. This paper is a tour of the functions encountered; they have been expressed in C++ (and could be expressed in other languages such as Fortran or C). With the help of a good optimizing compiler, floating-point efficiency is much closer to 100%. The protein folding application was initially run by the life science researchers on IBM POWER3™ machines while the computer science researchers were designing and bringing up the Blue Gene/L hardware. Some of the work discussed resulted in enhanced compiler optimizations, which now improve the performance of floating-point-intensive applications compiled by the IBM VisualAge® series of compilers for POWER3, POWER4™, POWER4+™, and POWER5™. The implementations are offered in the hope that they may help in other implementations of molecular dynamics or in other fields of endeavor, and in the hope that others may adapt the ideas presented here to deliver additional mathematical functions at high throughput.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.112 | 0.066 |
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".