An Efficient Collaborative Communication Mechanism for MPI Neighborhood Collectives
Bibliographic record
Abstract
Neighborhood collectives are introduced in MPI3.0 standard to provide users with the opportunitv to define their own communication patterns through the process topologv interface of MPI. In this paper, we propose a collaborative communication mechanism based on common neighborhoods that might exist among groups of k processes. Such common neighborhoods are used to decrease the number of communication stages through message combining. We show how designing our desired communication pattern can be modeled as a maximum weighted matching problem in distributed hvpergraphs, and propose a distributed algorithm to solve it. Moreover, we consider two design alternatives: topologvagnostic and topologv-aware. The former ignores the phvsical topologv o7 the svstem and the mapping o7 processes, whereas the latter takes them into account to further optimize the communication pattern. Our experimental results show that we can gain up to 8x and 5.2x improvement for various process topologies and a SpMM kernel, respectivelv.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".