Bibliographic record
Abstract
Parallel independent disks can enhance the performance of external memory (EM) algorithms, but the programming task is often difficult. Each disk can service only one read or write request at a time; the challenge is to keep the disks as busy as possible. In this article, we develop a randomized allocation discipline for parallel independent disks, called randomized cycling . We show how it can be used as the basis for an efficient distribution sort algorithm, which we call randomized cycling distribution sort (RCD). We prove that the expected I/O complexity of RCD is optimal. The analysis uses a novel reduction to a scenario with significantly fewer probabilistic interdependencies. We demonstrate RCD's practicality by experimental simulations. Using the randomized cycling discipline, algorithms developed for the unrealistic multihead disk model can be simulated on the realistic parallel disk model for the class of multipass algorithms, which make a complete pass through their data before accessing any element a second time. In particular, algorithms based upon the well-known distribution and merge paradigms of EM computation can be optimally extended from a single disk to parallel disks.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".