Tale of Two Cs: Computation vs. Communication Scaling for Future Transformers on Future Hardware
Bibliographic record
Abstract
Scaling neural network models has delivered dramatic quality gains across ML problems. However, this scaling also increased the reliance on efficient distributed training techniques. Accordingly, like other distributed computing scenarios, it is important to understand how compute and communication will scale relative to one another as models scale and hardware evolves? A careful study which answers this question can better guide the design of future systems which can efficiently train future large models.Accordingly, we comprehensively analyze compute vs. communication (Comp-vs.-Comm) scaling for future Transformer models on future hardware, across multiple axes (algorithmic, empirical, hardware evolution). First, our algorithmic analysis shows that compute generally enjoys an edge over communication as models scale. However, these trends are being stressed since device memory capacity scales much slower than model size. We quantify this edge by empirically studying how Comp-vs.-Comm scales for future models on future hardware. To avoid profiling numerous Transformer models across many setups, we extract execution regions and project costs using operator models. This allows a spectrum (hundreds) of future model/hardware scenarios to be accurately studied (< 15% error) and reduces profiling costs by 2100×. Our experiments show that communication will be a significant portion (40-75%) of runtime as models and hardware evolve. Moreover, communication that is often hidden by overlapped computation in today’s models cannot be hidden in future, larger models. Overall, this work highlights communication’s increasingly large role as models scale, discusses promising techniques to potentially tackle communication, and discusses how our analysis influences their potential improvements.
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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 | 0.002 | 0.015 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".