Graph Trace Analysis Approach to Optimizing Power and Heat Flow for Clustered Computing; An Example of Model Based System of Systems Design and Deployment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 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 it