Run-time power-gating in caches of GPUs for leakage energy savings
Bibliographic record
Abstract
In this paper, we propose a novel microarchitectural technique for run-time power-gating caches of GPUs to save leakage energy. The L1 cache (private to a core) can be put in a low-leakage sleep mode when there are no ready threads to be scheduled, and the L2 cache can be put in sleep mode when there is no memory request. The sleep mode is state-retentive, which precludes the necessity to flush the caches after they are woken up. The primary reason for the effectiveness our technique lies in the fact that the latency of detecting cache inactivity, putting a cache to sleep and waking it up before it is accessed, is completely hidden microarchitecturally. The technique incurs insignificant overheads in terms of power and area. Experiments were performed using the GPGPU-Sim simulator on benchmarks that was set up using the CUDA framework. The power and latency modeling of the cache arrays for measuring the wake-up latency and the break-even periods is performed using a 32-nm SOI IBM technology model. Based on experiments on 16 different GPU workloads, the average energy savings achieved by the proposed technique is 54%.
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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 | 0.000 |
| 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 | 0.000 |
| 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".