RDMA-based and SMP-aware Multi-port All-Gather on Multi-rail QsNet^II SMP Clusters
Bibliographic record
Abstract
Clusters of symmetric multiprocessors (SMP) are more commonplace than ever in achieving high- performance. Scientific applications running on clusters employ collective communications extensively. Using shared memory communication among co- located processes on SMP nodes as well as remote direct memory access (RDMA) operations for inter- node communication and trying to overlap them is a proven technique in boosting the performance of collective operations. The effect is much more pronounced when efficient multi-port collectives on multi-rail networks are devised and implemented. In this work, we design and implement multi-port RDMA-based and SMP-aware all-gather algorithms with message striping over multi-rail QsNeIIdirectly at the Elan level. We compare our algorithms against RDMA-only traditional algorithms and the native elan_gather(). Our performance results indicate that the proposed SMP-aware Brack all-gather gains an improvement of up to 1.96 for 4KB messages over the native elanjgather(). Meanwhile, the direct algorithm achieves up to 1.49 improvement for 32 KB messages.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".