Concurrent and distributed data structures for multikey sorting on computer clusters
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
Summary form only given. This paper focuses on theoretical and practical aspects of the high-performance multikey sorting problem on computer clusters, with particular emphasis on the Alpha Maci Cluster, a world-class high-performance supercomputer that has many processors interconnected by a wide range of high-speed network connections. Even though the focus of this paper is on multikey sorting problems, developing new data structures and techniques for designing high-performance algorithms on computer clusters are of both theoretical and practical interest. We investigate strategies for developing, implementing, and refining high-performance algorithms for sorting multi-dimensional data on computer clusters. In addition, maximizing the performance of such distributed memory machines requires efficient data structures coupled with good load balancing.
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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 | 0.001 |
| 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