Efficient generated libraries for asynchronous derivative computation
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Bibliographic record
Abstract
The computation of derivatives via automatic differentiation is a valu-able technique in many science and engineering applications. While the implementation of automatic differentiation via source transformation yields the highest-efficiency results, the implementation via operator over-loading remains a viable alternative for some application contexts, such as the computation of higher-order derivatives or in cases where C++ still proves to be too complicated for the currently available source transfor-mation tools. The Rapsodia code generator creates libraries that overload intrinsics for derivative computation. In this paper, we discuss modifica-tions to Rapsodia to improve the efficiency of the generated code, first via limited loop unrolling and second via multithreaded asynchronous derivative computation. We introduce the approaches and present run-time results. 1
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it