Characterizing the performance benefits of fused CPU/GPU systems using FusionSim
Why this work is in the frame
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Bibliographic record
Abstract
We use FusionSim to characterize the performance of the Rodinia benchmarks on fused and discrete systems. We demonstrate that the speed-up due to fusion is highly correlated with the input data size. We demonstrate that for benchmarks that benefit most from fusion, a 9.72x speed up is possible for small problem sizes. This speedup reduces to 1.84x with medium or large problem sizes. We study a simple, software-managed coherence solution for the fused system. We find that it imposes a minor performance overhead of 2% for most benchmarks and as high as 5% for some. Finally, we develop an analytical model for the performance benefit that is to be expected from fusion for applications with a simple communication and computation pattern and show that FusionSim follows the predicted performance trend.
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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 | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it