Parallel simulation of mixed-abstraction SystemC models on GPUs and multicore CPUs
Why this work is in the frame
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Bibliographic record
Abstract
This work presents a methodology that parallelizes the simulation of mixed-abstraction level SystemC models across multicore CPUs, and graphics processing units (GPUs) for improved simulation performance. Given a SystemC model, we partition it into processes suitable for GPU execution and CPU execution. We convert the processes identified for GPU execution into GPU kernels with additional SystemC wrapper processes that invoke these kernels. The wrappers enable seamless communication of events in all directions between the GPUs and CPUs. We alter the OSCI SystemC simulation kernel to allow parallel execution of processes. Hence, we co-simulate in parallel, the SystemC processes on multiple CPUs, and the GPU kernels on the GPUs; exploit both the CPUs, and GPUs for faster simulation. We experiment with synthetic benchmarks and a set-top box case study.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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