Workload-Aware Power Gating Design and Run-Time Management for Massively Parallel GPGPUs
Bibliographic record
Abstract
Power gating (PG) is an effective power efficiency improvement technique. Future general-purpose graphics processing units (GPGPUs) will likely feature hundreds of compute units (CUs) and be power constrained, which leads to serious challenges to existing PG methodologies. In this paper, we propose novel design-time and run-time techniques to effectively implement power gating in future GPGPUs. Based on industrial models/measurement facilities, we show that designers must consider run-time parallelism within potential applications while implementing power gating designs to avoid incurring unnecessary design overheads. By scaling measurements from a real 28nm GPGPU to a hypothetical future 10nm node, we show that a PG granularity of 16 CU/cluster achieves 99% peak run-time performance without the excessive 53% design-time area overhead of per-CU PG. We also demonstrate that a run-time power management algorithm that is aware of the PG granularity leads to up to 18% additional performance through frequency-boosting under thermal-design power (TDP) constraints.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".