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 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 | 0.001 |
| Open science | 0.003 | 0.002 |
| 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".