Analysis and evaluation of greedy thread swapping based dynamic power management for MPSoC platforms
Bibliographic record
Abstract
Thread migration (TM) is a recently proposed dynamic power management technique for heterogeneous multi-processor system-on-chip (MPSoC) platforms that eliminates the area and power overheads incurred by fine-grained dynamic voltage and frequency scaling (DVFS) based power management. In this paper, we take the first step towards formally analyzing and experimentally evaluating the use of power-aware TM for parallel data streaming applications on MPSoC platforms. From an analysis perspective, we characterize the optimal mapping of threads to cores and prove the convergence properties of a complexity effective greedy thread swapping based TM algorithm to the globally optimal solution. The proposed techniques are evaluated on a 9-core FPGA based MPSoC prototype equipped with fully-functional TM and DVFS support, and running a parallelized video encoding benchmark based on the Motion Picture Experts Group (MPEG-2) standard. Our experimental results validate the proposed theoretical analysis, and show that the proposed TM algorithm provides within 8% of the DVFS performance under the same power budget, and assuming no overheads for DVFS. Assuming voltage regulator inefficiency of 80%, the proposed TM algorithm has 9% higher performance than DVFS, again under the same total power budget.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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".