Power-Efficient Multiple Producer-Consumer
Bibliographic record
Abstract
Power efficiency has been one of the main objectives of hardware design in the last two decades. However, with the recent explosion of mobile computing and the increasing demand for green data centers, software power efficiency has also risen to be an equally important factor. We argue that most classic concurrency control algorithms were designed in an era when power efficiency was not an important dimension in algorithm design. Such algorithms are applied to solve a wide range of problems from kernel-level primitives in operating systems to networking devices and web services. These primitives and services are constantly and heavily invoked in any computer system and by larger scale in networking devices and data centers. Thus, even a small change in their power spectrum can make a huge impact on overall power consumption in long periods of time. This paper focuses on the classic producer-consumer problem. First, we study the power efficiency of different existing implementations of the producer-consumer problem. In particular, we present evidence that these implementations behave drastically differently with respect to power consumption. Secondly, we present a dynamic algorithm for the multiple producer-consumer problem, where consumers in a multicore system use learning mechanisms to predict the rate of production, and effectively utilize this prediction to attempt to latch onto previously scheduled CPU wake-ups. Such group latching results in minimizing the overall number of CPU wakeups and in effect, power consumption. We enable consumers to dynamically reserve more pre-allocated memory in cases where the production rate is too high. Consumers may compete for the extra space and dynamically release it when it is no longer needed. Our experiments show that our algorithm provides up to 40% decrease in the number of CPU wakeups, and 30% decrease in power consumption. We validate the scalability of our algorithm with an increasing number of consumers.
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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 | 0.000 | 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 | 0.001 |
| 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".