Virtual Single-Core for Multicore Real-Time Computing
Bibliographic record
Abstract
This paper introduces Virtual Single-Core (VSC) technology that allows engineers to use a group of cores in a multicore computer as if the group of cores were a larger single-core computer. \n \nMulticore technology has many benefits, such as increased CPU bandwidth per chip. However, when used as is, inter-core interferences can be severe. Because of the potential for large and random delay spikes, the U.S. Federal Aviation Administration (FAA), European Aviation Safety Agency (EASA), and Transport Canada specify that only one core can be used, unless inter-core interference is specifically defined and handled. In addition, DO-178C: Software Considerations in Airborne Systems and Equipment Certification is for single-core chips only. \nSingle-core Equivalence (SCE) technology partitions the resources shared by cores in such a way that each core can be used as if it were a single-core computer. SCE is an effective solution that address certification authorities' concerns of intercore interference. However, a core in a multicore chip is often slower than a fast single-core chip. Therefore, a large multi-thread (task) application may not be scheduled within a core. \nVirtual Single-Core (VSC) technology extends the SCE technology so that a group of cores can be used to schedule a large application as if the VSC were a larger single-core computer. VSC greatly facilities the migration of certified avionics software from single-core computers to multicore computers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".