A performance comparison of hierarchical ring- and mesh-connected multiprocessor networks
Bibliographic record
Abstract
This paper compares the performance of hierarchical ring- and mesh-connected wormhole routed shared memory multiprocessor networks in a simulation study. Hierarchical rings are interesting alternatives to meshes since (i) they can be clocked at faster rates, (ii) they can have wider data paths and hence shorter message sates, (iii) they allow addition and removal of processing nodes at arbitrary locations, (iv) their topology allows natural exploitation in the spatial locality of application memory access patterns, and (v) their topology allows efficient implementation of broadcasts. Our study shows that for workloads with little locality, meshes scale better than ring networks because ring-based systems have limited bisection bandwidth. However, for workloads with some memory access locality hierarchical rings outperform meshes by 20-40% for system sizes of up to 128 processors. Even with poor access locality, hierarchical rings will outperform meshes for these system sizes if the mesh router buffers are only 1-flit large, and they will outperform meshes an systems with less than 36 processors regardless of mesh router buffer size.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".