Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The main objective of this paper is to speed up the simulation performance of SystemC designs at the RTL abstraction level by exploiting the high degree of parallelism afforded by today's general purpose graphics processors (GPGPUs). Our approach parallelizes SystemC's discrete-event simulation (DES) on GPGPUs by transforming the model of computation of DES into a model of concurrent threads that synchronize as and when necessary. Unlike the cooperative threading model employed in the SystemC reference implementation, our threading model is capable of executing in parallel on the large number of simple processing units available on GPUs. Our simulation infrastructure is called SCGPSim1 and it includes a source-to-source (S2S) translator to transform synthesizable SystemC models into parallelly executable programs targeting an NVIDIA GPU. The translator retains the simulation semantics of the original designs by applying semantics preserving transformations. The resulting transformed models mapped onto the massively parallel architecture of GPUs improve simulation efficiency quite substantially. Preliminary experiments with varying-sized examples such as AES, ALU, and FIR have shown simulation speed-ups ranging from 30x to 100x. Considering that our transformations are not yet optimized, we believe that optimizing them will improve the simulation performance even further.
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.
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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 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