EFFICIENT ALGORITHMS FOR SELECTION AND SORTING OF LARGE DISTRIBUTED FILES ON DE BRUIJN AND HYPERCUBE STRUCTURES
Bibliographic record
Abstract
In this paper we show the power of sampling techniques in designing efficient distributed algorithms. In particular, we apply sampling techniques in the design of selection algorithms on the hypercube and de Bruijn networks, and show that the message complexity of selecting an item from a set (file) is less sensitive to the cardinality of the set (file). Given a file with n keys, our algorithm performs a selection on a p-node de Bruijn network or hypercube using only O(p log log n) messages and suffering a delay of O(τ log p log log n), with high probability. Our selection scheme outperforms the existing approaches in terms of both message complexity and communication delay. Because of the lesser sensitivity of message complexity and communication delay of our algorithms to the file size, our distributed selection schemes are very attractive in applications where very large database systems are involved. Using our selection algorithms, we also show that both quicksort-based sorting scheme and enumeration sorting scheme can be developed for sorting large distributed files on the hypercube and de Bruijn networks. Both of our sorting algorithms outperform the existing distributed sorting schemes in terms of both message complexity and communication delay.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 |
| 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 | 0.000 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".