An automatic trace based performance evaluation model building for parallel distributed systems
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
Performance models can be built at early stages of software development cycle to aid software designers to assess design alternatives and identify fundamental design pitfalls before the implementation phase starts. These models are flexible for varying operational conditions and design alternatives; however, their creation is not trivial and requires considerable efforts. This paper addresses this problem by introducing automation in process of Layered Queuing Network (LQN) performance model creation for traces of events generated from instrumented software programs in the nodes of a distributed parallel software application. The event-traces are created based on a new timestamp format, which is independent of physical time and uses extremely low count elements. A set of post-mortem methodologies have been introduced to identify the interactions between the service nodes of the parallel distributed software application and determine their workload activities, while supporting concurrent executions in the nodes. It can capture Forward, Asynchronous, Synchronous and loops of Asynchronous or Forward interactions. The final result is a framework of methodologies, specifications and tools which is appropriate for model-based performance evaluation parallel distributed software applications.
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 | 0.002 | 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 | 0.001 |
| 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 it