The Performance Cost of Disintegrated Manycores: Which Applications Lose and Why?
Bibliographic record
Abstract
Recent industry manycores have transitioned to disintegrated designs with multiple chips within a package. Disintegration can make larger and higher performance systems economically viable by reducing cost, but introduces additional network bandwidth and latency bottlenecks which harms performance. Ideally, cost savings outweigh disintegration slowdown. This thesis presents the first study, to our knowledge, of the disintegration performance penalty across a diverse suite of applications and a characterization of what properties of applications impact this penalty. We find high variance in disintegration slowdown (performance penalty normalized to equivalently sized monolithic design) across applications. Some disintegrated applications lose almost half their performance. We identify that metrics relating to the network-on-package bandwidth and data sharing are correlated with disintegration slowdown. Disintegration constrains the network where it crosses between chips, and these categories of metrics either measure network pressure or measure data sharing, which causes network pressure.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".