Resource Management in a Large Reconfigurable Transputer-based System
Bibliographic record
Abstract
This paper describes two of the intijor components of TIPS, a Transputer-based Iiirteractive Parallelizing System, under development at UBC. The system runs on a 74 node transpuier system iiilercorinectcd by crossbar switches with inulliple liiihs to ike host, a SUN-4. It uses Trollaus with the Logical Syslems C compiler. The first component described is TMRP, a topology independent mapping facility. TMAP’s objective is to automate the mapping process, and muhe it independent from changes an the underlying architecture. It integrates two large pieces of soft,ware, Trollivs and Prep-p. We describe its design and discuss specific problems in trying to achieve a machine iudependent environment. The second com.poiient described is TRES, a higher level reso’urce managernelit facility. TRES is based on parameterized models of coniputation which are used 20 predict perforni.nnce and optimize the use of machine resources. The user need only specify the model (i.e. prograinmin,g paradigm) and the computational task to be performed. TRES determines the optimal topology and number of processors to use. This inforniation is used by the TMAP system.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".