Harmonics: Scalable Collective Scheduling in Multi-Tenant GPU Clusters
Bibliographic record
Abstract
Distributed machine learning (DML), such as large language model (LLM) training, has become one of the most critical services in multi-tenant cloud computing. However, communication contention among concurrent DML jobs significantly degrades overall GPU utilization, leading to inefficient training cluster performance. Existing approaches either achieve high performance at the cost of long scheduling runtime or reduce scheduling time at the expense of poor performance. We present Harmonics, a novel two-tier scheduling framework that strikes a balance between scheduling latency and performance. It coordinates decisions between Local Schedulers and a lightweight Global Coordinator to enable scalable and adaptive scheduling. By combining rack-level epoch-based optimization with global coordination, Harmonics alleviates communication contention and improves resource efficiency. We implement and evaluate Harmonics on real distributed ML workloads running on a GPU testbed. Compared to state-of-the-art methods such as fair sharing, optimal scheduling, Crux, and Cassini, Harmonics reduces training time by up to 33% and communication time by up to 48%. Large-scale simulations show that it reduces scheduling time by up to 91× while improving training time by 26% in large-cluster settings.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".