Bibliographic record
Abstract
As circuit sizes increase, a means to improve the performance of simulations is constantly demanded, without sacrificing the accuracy of the results.Traditionally, improvements were obtainable through the raw increase of computational power of computing platforms, which allow the sequential algorithms used in circuit simulation to perform at a faster rate.Recently, however, these performance improvements are no longer obtained through the improvement of individual processors, but rather by including more processors in the system.The result is that higher performance is now obtained via exploiting multicore processors, distributed clusters, and cloud platforms.Traditional sequential algorithms cannot take advantage of the improvements promised by these new systems.Existing parallel algorithms for circuit simulation are based on the domain decomposition approach.However, it has been demonstrated that domain decomposition suffers scalability problems as the number of processors in a system increases.To address this problem, a new parallel circuit simulation algorithm is presented that allows modern multicore and distributed processors to be exploited to realize this performance improvement.These improvements are obtained without sacrificing accuracy or resorting to iterative techniques.The scalability improvements using the proposed algorithm have been demonstrated through the consideration of several industrial examples.i UMA Uniform Memory Access, a system architecture for multi-processor systems where the cost of accessing a particular memory address is the same for each processor in the system.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".