HiPIPE: Adaptive-Chunk Pipeline Scheduling for Hierarchical Collective Communication
Bibliographic record
Abstract
Multi-dimensional networks have become the standard for interconnecting GPUs to support large-scale distributed machine learning (ML) training, driven by increasing computational and memory demands. However, the inherent bandwidth heterogeneity across network dimensions poses a significant challenge in efficiently scheduling collective operations, such as AllReduce, to minimize communication time. Existing hierarchical approaches suffer from inefficiencies due to static or suboptimal pipeline structures with uniform-sized chunks. We propose HiPIPE, a novel solution for multi-dimensional All-Reduce collective communication, incorporating three key optimizations: chunk scheduling across all dimensions, adaptive chunk sizing, and dynamic chunk number selection. These techniques collectively enhance communication efficiency and maximize bandwidth utilization. Particularly, we formulate chunk size optimization as a linear programming problem to derive the optimal chunk sizes, significantly reducing pipeline bubbles and improving overall performance. Extensive experiments using real-testbed emulation demonstrate that HiPIPE consistently outperforms state-of-the-art (SOTA) approaches across diverse network topologies and configurations, achieving an average communication time reduction of 28.85% while sustaining bandwidth utilization of up to 98.11%.
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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 | 0.001 | 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".