Energy-Efficient Multiple Producer-Consumer
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Bibliographic record
Abstract
Hardware energy efficiency has been one of the prominent objectives of system design in the last two decades. However, with the recent explosion in mobile computing and the increasing demand for green data centers, software energy efficiency has also risen to be an equally important factor. The majority of classic concurrency control algorithms were designed in an era when energy efficiency was not an important dimension in algorithm design. Concurrency control 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 computing system and by a larger scale in networking devices and data centers. Thus, even a small change in their energy spectrum can make a huge impact on overall energy consumption for long periods of time. This paper focuses on the classic producer-consumer problem. First, we study the energy profile of a set of existing producer-consumer algorithms. In particular, we present evidence that although these algorithms share the same functional goals, their behavior with respect to energy consumption are drastically different. Then, 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 increases the idle time between consumer activations resulting in more CPU idle time and, hence, lower average CPU frequency. This in turn reduces energy 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 a 38 percent decrease in energy consumption compared to a mainstream semaphore-based producer-consumer implementation when running 10 parallel consumers. We validate the effectiveness of our algorithm with a set of thorough experiments on varying parameters of scalability. Finally, we present our recommendations on when our algorithm is most beneficial.
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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 | 0.000 | 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 | 0.001 | 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