Efficient RDMA-based multi-port collectives on multi-rail QsNet/sup II/ clusters
Bibliographic record
Abstract
Many scientific applications use MPI collective communications intensively. Therefore, efficient and scalable implementation of collective operations is critical to the performance of such applications running on clusters. Quadrics QsNet/sup II/ is a high-performance interconnect for clusters that implements some collectives at the Elan level. These collectives are directly used by their corresponding MPI collectives. Quadrics software supports point-to-point striping over multi-rail QsNet/sup II/ networks. However, multi-rail collectives have not been supported. In this work, we propose a number of RDMA-based multi-port collectives over multi-rail QsNet/sup II/ clusters directly at the Elan level. Our performance results indicate that the proposed multi-port gather gains an improvement of up to 6.35 for 1MB message over the native elan/spl I.bar/gather. The proposed multi-port all-to-all performs better than the native elan/spl I.bar/alltoall by a factor of 2.19 for 16KB message. Moreover, we have also proposed two algorithms for the scatter operation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".