Companion Publication for ACM/SPEC on International Conference on Performance Engineering
Bibliographic record
Abstract
The 7th ACM/SPEC International Conference on Performance Engineering (ICPE 2016) takes place in Delft in The Netherlands in March 2016. The conference grew out of the ACM Workshop on Software Performance (WOSP since 1998) and the SPEC International Performance Engineering Workshop (SIPEW since 2008), with the goal of integrating theory and practice in the field of performance engineering. It is a great pleasure for us to offer an outstanding technical program this year, which we believe will allow researchers and practitioners to present their visions and latest innovation, and to exchange ideas within the community. Overall, we received 89 high quality submissions across all three tracks. The main Research Track attracted 57 submissions with 19 accepted (33% acceptance rate) for presentation at the conference. Among them were 16 full papers and three short papers. Each paper received at least three reviews from experienced program committee members. In the Work-In-Progress and Vision Track, six out of 15 contributions were selected. The Industry and Experience Track received 17 submissions, of which seven were selected for inclusion in the program. The accepted papers were organized into five research track sessions, two industry track sessions, and one WiP and vision track session. Three best paper candidates were also selected: two research papers and one industry paper. We are proud to have three excellent keynote speakers as part of our technical program: Bianca Schroeder from University of Toronto, Canada, presenting Case studies from the real world: The importance of measurement and analysis in building better systems Wilhelm Hasselbring from Kiel University, Germany, discussing Microservices for Scalability Angelo Corsaro, Chief Technology Officer at PrismTech, talking about Cloudy, Foggy and Misty Internet of Things In addition, the program includes four tutorials, a doctoral symposium, a poster and demo track, the SPEC Distinguished Dissertation Award, and three interesting workshops, including the International Workshop on Large-Scale Testing (LT), the 2nd International Workshop on Performance Analysis of Big data Systems (PABS), and the 2nd Workshop on Challenges in Performance Methods for Software Development (WOSPC). The program covers traditional ICPE topics such as software and systems performance modeling and prediction, analysis and optimization, characterization and profiling, as well as application of performance engineering theory and techniques to several practical fields, including distributed systems, cloud computing, storage, energy, big data, virtualized systems and containers.
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.453 | 0.309 |
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".