Cephalo: Harnessing Heterogeneous GPU Clusters for Training Transformer Models
Bibliographic record
Abstract
Training transformer models requires substantial GPU compute and memory resources.While training systems are typically designed for homogeneous GPU clusters, sufficiently large homogeneous clusters are difficult to acquire for most organizations due to cost and GPU scarcity.Hence, it is increasingly common to assemble heterogeneous clusters with a mix of higher and lower-end GPUs featuring differing compute power and memory capacity.Existing methods attempt to distribute the workload across heterogeneous GPUs based on compute capacity but often underutilize compute due to memory constraints.We present Cephalo, a system that holistically balances both compute and memory usage by decoupling compute distribution from training state assignment.Cephalo uses an optimizer to efficiently distribute the compute workload and storage of training state to account for GPU heterogeneity in the cluster.Additionally, it separates memory from compute requirements through an optimized gradient accumulation strategy.Compared to state-of-theart methods, Cephalo achieves 1.2×-10.8×higher training throughput while supporting larger models and batch sizes.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".