Visualizing systems and software performance - Report on the GI-Dagstuhl seminar for young researchers, July 9-13, 2018
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
This GI-Dagstuhl seminar addressed the problem of visualizing performance-related data of systems and the software that they run. Due to the scale of performance-related data and the open-ended nature of analyzing it, visualization is often the only feasible way to comprehend, improve, and debug the performance behaviour of systems. The rise of cloud and big data systems, and the rapidly growing scale of the performance-related data that they generate, have led to an increased need for visualization of such data. However, the research communities behind data visualization, performance engineering, and high-performance computing are largely disjunct. The goal of this seminar was to bring together young researchers from these research areas to identify cross-community collaboration and to set the path for long-lasting collaborations towards rich and effective visualizations of performance-related data.
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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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