Mobile Parallel Computing Algorithms for Single-Buffered, Speed-Scalable Processors
Bibliographic record
Abstract
This paper synthesizes and simulates two task-allocation algorithms that run in real time to optimally determine which processor among the multiple (single-buffered) processors in a mobile device should locally process an incoming stream of hypothetical tasks. By using speed-scaling, where each processor's speed is able to change within hardware and software processing constraints, the algorithms also explicitly determine the optimum processing rate of executing each hypothetical task. Hypothetical tasks could be heterogeneous and is each defined in an abstract, general form by considering its computation volume, processing and memory requirements. The time and energy dimensions of executing each hypothetical task is modeled in a cost function that is each associated with a processing stream. Both algorithms allow the user to specify the unit cost of energy and time for executing each hypothetical task. One algorithm extends the functionality of the other by allowing the user or the OS of the mobile device to further modify a task's unit cost of time or energy in order to achieve a linearly controlled operation point. This operation point lies somewhere in the economy-performance mode continuum of a task's execution. We focus on single buffer, single-threading where a single task is allocated to a given processor and is processed until its completion. For diverse application, we also assume that the processors/cores are heterogeneous in that they may differ in their hardware specifications with respect to maximum processing rate and energy inefficiency coefficient.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".