Bibliographic record
Abstract
Abstract One of the important factors that governs the performance of a parallel computer system is the algorithm that determines the routing of messages in the interconnection network. There have been a number of routing algorithms proposed in the literature for various interconnection networks. The objective of this paper is to present a scheme for classification of routing algorithms on direct networks. The classification scheme is based on five main categories: hardware specification, destination type, worst-case characteristics, fault-tolerance, and performance. To illustrate its use, we have applied it to several previously proposed routing algorithms. It is shown that the scheme serves as a powerful tool in comparing the performance features of routing algorithms for direct networks. The taxonomy summarizes routing techniques, showing what features it lacks, and where extension would be worthwhile. For example, we observe (by classifying it) that Algorithm A1 is an effective hypercube routing algorithm; however, its large header size causes it to perform poorly. This leads to an improvement that drastically reduces the header size. Furthermore, we see how one can combine routing algorithms to create general hybrid strategies. We thus hope that our classification scheme will facilitate research in the area of routing algorithm development. 1 Introduction The implementation of many parallel and distributed algorithms on multiprocessor systems requires intensive communications between processors 1;2;3;4. Consequently, one of the dominating factors that governs the performance of a parallel computer system is the underlying interconnection network and the associated algorithms that determine the routing of messages from one processor to another. Static interconnection networks, also known as direct networks 1, are a popular class of interconnection networks which are characterized by static links (or channels) conforming to a specific topology. Direct networks have been employed in many commercial parallel machines such as the Cray T3E, CM-5, Tera supercomputer, nCUBE, and Intel Paragon, iPSC and iWarp 1;5.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.734 | 0.478 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".