Bibliographic record
Abstract
iWAPT (International Workshop on Automatic Performance Tuning) is a series of workshops that focus on research and techniques related to performance sustainability issues.The series provides an opportunity for researchers and users of automatic performance tuning (AT) technologies to exchange ideas and experiences acquired when applying such technologies to improve the performance of algorithms, libraries, and applications; in particular, on cutting edge computing platforms.The workshop is particularly interested in autotuning and its relationship to the following topic areas, among others:Machine-adaptive algorithms Automatic program generation Performance analysis and modeling Adaptive numerical algorithms and libraries Multi-and manycore systems, heterogeneous architectures challenges Compilation strategies (e.g.iterative and empirical compilers) Programming models Runtime systems Empirical search heuristics Power-and/or energy-aware computing Applying machine learning to autotuning iWAPT2023 is the eighteenth in a series of successful workshops devoted to AT.The series started in Tokyo in 2006.Since then, it has been held every year: six times in Japan, four times in the USA, once in Singapore, India, Spain, Canada and Brazil, and three times virtually (2020, 2021, and 2022).In particular, iWAPT has been hosted with IPDPS since 2015.iWAPT2023 is a full-day workshop consist of keynote speaker presentation, invited speaker presentations, and 30-minute presentations of research papers.We received seven submissions to the workshop.
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 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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.333 | 0.272 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".