Bibliographic record
Abstract
Parallel computing plays an increasingly important role in the field of numerical computing, with technological developments permitting an ever-increasing range of potential applications. However, the design of parallel numerical programs is far more difficult than serial programs, especially in the face of complex application problems, the development and performance optimization of massive parallel numerical programs are very challenging. High-performance numerical software often requires a comprehensive and long exploration process from design to implementation. With the help of existing parallel algorithm packages, this paper develops efficient finite element parallel programs without accounting for complex data distribution and communication. In doing so, this method greatly reduces the difficulty and cost of finite element parallel computing and shortens the development cycle. This paper makes a series of optimizations for algorithms and computational processes to improve their stability, computational efficiency, and parallel scalability. Subsequently, the algorithm is made suitable for solving the eigenvalues of a large-scale sparse matrix in a parallel computing environment. The software package formed in this paper depends neither on the specific structure of the matrix nor on the vector, meaning it can be applied to arbitrary matrix-vector structures. The test results for several typical matrices show that the algorithm and software package have not only good numerical stability and scalability, but also a greater improvement in efficiency when compared with other parallel solvers.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".