Fast deterministic sorting on large parallel machines
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Many sorting algorithms that perform well on uniformly distributed data suffer significant performance degradation on non-random data. Unfortunately many real-world applications require sorting on data that is not uniformly distributed. In this paper we consider distributions of varying entropies. We describe A-Ranksort, a new sorting algorithm for parallel machines, whose behavior on input distributions of different entropies is relatively stable. Our algorithm is based on a deterministic strategy to find approximate ranks for all keys. We implemented A-Ranksort, B-Flashsort, Radixsort, and Bitonic sort on a 2048 processor Maspar MP-1. Our experiments show that A-Ranksort out-performs all the other algorithms on a variety of input distributions, when the output is required to be balanced. We are also able to provide bounds on the average-case and worst-case complexities of our algorithm, in terms of the costs of some chosen primitive operations. The predicted performance is very close to the empirical results, thus justifying our model.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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