Reducing Static and Dynamic Power of L1 Data  Caches in GPGPUs
Bibliographic record
Abstract
With the widespread adoption of GPGPUs for general purpose computing domain, the size of GPGPUs has grown quickly, making power consumption a major bottleneck. L1 data caches boost performance of processors by hiding latency of memory but consume significant power as they need to serve many processing cores. We propose two optimization techniques to reduce static and dynamic power of L1 data caches in GPGPUs.The first optimization technique reduces static power of L1 data cache by placing cache blocks into drowsy mode immediately after each access. In GPGPUs, the cache blocks are idle for long intervals. Hence, moving a cache block into drowsy state immediately after each access reduces leakage power significantly with negligible performance impact. The second optimization technique targets dynamic power of L1 data cache. Due to branch divergence, threads within a warp may follow different paths of execution. This may result in inactive threads within a warp. Existing GPGPUs access the whole cache blocks, ignoring inactive threads within a warp. We use active mask of GPGPUs and access only the portion of cache blocks that are required by active threads. By dynamically disabling unnecessary sections of cache blocks, we are able to reduce dynamic power of caches.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".