Graph Trace Analysis Approach to Optimizing Power and Heat Flow for Clustered Computing; An Example of Model Based System of Systems Design and Deployment
Bibliographic record
Abstract
During system of system (SoS) design, many decisions from different engineering disciplines are made and documented. Today, designers increasingly use modeling. For example, they address the issues involved with total cost of ownership by better understanding the interactions of parts and systems. For a large SoS, the labor intense modeling has one potential alternative approach involving the concepts of graph trace analysis (GTA) for distributed processing of integrated system models (ISM). GTA features a wide range of algorithms that flexibly attach to models that stay in their 'engineering domains' in the ISM. A subsystem model for a single domain can be built directly from the engineering design data and then simulated or analyzed. A model using millions of simple objects can be built automatically, including looped and radial systems. Physical dependency linkages between engineering domains can be built directly from reference designators and parts data. Design and deployment of a compute cluster is illustrated by composing an "integrated system model" (ISM) from different engineering domain models, where models from different domains are linked together with dependency iterators from GTA. During early design phases, the ISM of a compute cluster might include a short list of engineering design domains. Later, during deployment, sensors and actuators are included in the ISM and GTA algorithms. A need for open, standard parts data is discussed for design tools used in engineering along with data exchange formats that allow product design data to be reused by other design teams.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".