Benchmarking of a distributed-memory, high-order discontinuous finite element flow solver on a shared-memory parallel architecture
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
High-order numerical schemes implemented on high-performance parallel computers are of special interest for contemporary numerical simulations, especially in computational fluid dynamics. In this study, first, a high-order parallel flow solver is presented for some test cases of aerodynamic simulations. The flow solver is based on a discontinuous Galerkin finite element method on arbitrary grids with different orders of polynomial approximation for solving the compressible flow model. Second, the distributed-memory parallel implementation of the flow solver is benchmarked on a shared-memory multicore system. A distributed-memory parallel application can be executed on shared-memory architectures by assuming that each of the parallel processes assumes separate memory address space, although all are present in a common memory bank. This approach can offer an effective measure to address several issues related to limited resources, especially for uninterrupted electric supply. The scalability of the parallel application is analyzed by varying the problem workload per process for the test cases. For some test cases in the present study, over 90% parallel efficiency per process is also observed. The performance of the distributed-memory program on the shared-memory architecture establishes suitability and robustness of the approach for small to medium scale problems, at least.
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 | 0.000 | 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 | 0.000 |
| 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