Partitioning and Mapping of Mesh-Based Applications onto Computational Grids
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
Mesh-based applications, such as those that involve the numerical solution of partial differential equations, may be able to take advantage of the performance of computational grids. We require mesh partitioners that take the heterogeneity of the computational platform into account. Recent work in our group led to the creation of a heterogeneous mesh partitioner, PaGrid. We present a redesigned version of PaGrid, which uses estimated execution time as a cost function in all levels of multilevel refinement. It takes into account the characteristics of the application (computational complexity and size of messages) and of the computing platform (processor and network speeds), and balances the estimated execution time of processors. This results in partitions with up to 60% lower estimated execution times than METIS, a homogeneous partitioner, and similar improvements over JOSTLE, a heterogeneous partitioner. PaGrid achieves this in a reasonable amount of time, taking only two to three times longer than METIS.
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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 | 0.000 |
| 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