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 xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">II</sup> 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 xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">II</sup> 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 xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">II</sup> 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_gather. The proposed multi-port all-to-all performs better than the native elan_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 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 | 0.001 |
| 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".