An Efficient Transposition Algorithm for Distributed Memory Computers
Bibliographic record
Abstract
Data transposition is required in many numerical applications. When implemented on a distributed-memory computer, data transposition requires all-to-all communication, a time consuming operation. The Direct Exchange algorithm, commonly used for this task, is inefficient if the number of processors is large. We investigate a series of more sophisticated techniques: the Ring Exchange, Mesh Exchange and Cube Exchange algorithms. These data transposition schemes were incorporated into a parallel solver for the shallow-water equations. We compare the performance of these schemes with that of the Direct Exchange Algorithm and the MPI all-to-all communication routine, MPI_AllToAll. The numerical experiments were performed on a Cray T3E computer with 512 processors and on an ethernet-connected cluster of 36 Sun workstations. Both the analysis and the numerical results indicate that the more sophisticated Mesh and Cube Exchange algorithms perform better than either the simpler well-known Direct and Ring Exchange schemes or the MPI_AllToAll routine. We also generalize the Mesh and Cube Exchange algorithms to a d -dimensional mesh algorithm, which can be viewed as a generalization of the standard hypercube data transposition algorithm.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".