Fast deterministic sorting on large parallel machines
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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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".