Incorporating Latency in Heterogeneous Graph Partitioning
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
Parallel applications based on irregular meshes make use of mesh partitioners for efficient execution. Some mesh partitioners can map a mesh to a heterogeneous computational platform, where processor and network performance may vary. Such partitioners generally model the computational platform as a weighted graph, where the weight of a vertex gives relative processor performance, and the weight of a link indicates the relative transmission rate of the link between two processors. However, the performance of a network link is typically characterized by two parameters, bandwidth and latency, which cannot be captured in a single weight. We show that taking into account the network heterogeneity of a computational resource can significantly improve the quality of a domain decomposition obtained using graph partitioning. Furthermore, we show that taking into account bandwidth and latency of the network links is significantly better than just considering the former. This work is presented as an extension to the PaGridpartitioner, and includes a model for estimated execution time, which is used as a cost function by the partitioner but could also be used for performance prediction by application-oriented schedulers.
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 | 0.000 | 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