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.
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 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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".