OmNICCL: Zero-cost Sparse AllReduce with Direct Cache Access and SmartNICs
Bibliographic record
Abstract
AllReduce is a collective communication pattern commonly used in Distributed Deep Learning (DDL) and High Performance Computing (HPC). Sparse AllReduce, which compresses the data transmitted, achieves significant acceleration on specific workloads. However, compression introduces a non-negligible performance overhead. Therefore, we propose the OmNICreduce algorithm, an efficient inter-node sparse AllReduce method, as well as its implementation, OmNICCL. It utilizes Direct Cache Access (DCA) to achieve zero-overhead lossless compression and employs SmartNICs for aggregation on the data plane. We demonstrate that our method can provide up to a 7.24× speedup over conventional dense AllReduce methods under a 100Gbps RoCEv2 network and 1.76-17.37× performance improvement over state-of-the-art implementations when performing sparse AllReduce.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".