Adapting Operating Systems to Embedded Manycores: Scheduling and Inter-Process Communication
Bibliographic record
Abstract
The aim of this thesis is to adapt a bare minimum version of Enea’s OSE, a real-time operating system to Tilepro64, a 32-bit many-core processor. The tasks include mapping memory regions, context handling and interrupt management and building drivers for programmable interrupt controller and timer. Time-based scheduling on the Tilera port of OSE has been achieved. Furthermore, design proposals for improving inter-core data throughput in LINX for Linux have been suggested. The suggestions include using smart pointers and shared heaps as part of the user space thereby achieving a zero-copy mechanism. With reference to TILEPro64, consideration for leveraging the sharing of smart pointers using User Dynamic Network – a user accessible NoC – has been suggested in the adaptation design. Thus an inter-core process communication design involving minimal kernel involvement and reduced usage system calls was proposed. This thesis is part of Portable and Predictable Performance (PAPP) on Heterogeneous Embedded Manycores - an active research initiative undertaken by Advanced Research and Technology for Embedded Intelligent Systems (ARTEMIS). It is also partly associated with the Many-core programming and resource management for high-performance Embedded Systems (MANY) project and funded by Information Technology for European Advancement (ITEA2). The thesis work is carried out at Enea Software AB and XDIN AB.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".