Proceedings of the 18th ACM international symposium on High performance distributed computing
Bibliographic record
Abstract
It is our great pleasure to welcome you to the 18th ACM International Symposium on High Performance Distributed Computing. This year's symposium continues its tradition of being the premier forum for presentation of research results and experience reports on latest research findings on the design and use of parallel and distributed systems for high end computing, collaboration, data analysis, and other innovative applications. This installment takes place in Garching near Munich, Germany, June 11-13, 2009. Topics of interest include HPDC architectures, high end communications, data management and transport, software environments, operating system technologies, grid middleware, applications and algorithms, as well as fault tolerance. Co-located with HPDC 09 are six workshops. We welcome the Workshop on Challenges for Large Applications in Distributed Environments (CLADE 09), the Second International Workshop on Data-Aware Distributed Computing (DADC 09), the Workshop on Large-Scale System and Application Performance (LSAP 09), the Workshop on Monitoring, Logging and Accounting in Production Grids (MLA 09), the Workshop on Resiliency in High-Performance Computing (Resilience 09), and the 4th UPGRADE-CN Workshop on Content Management and Delivery in Large-Scale Networks (UPGRADE-CN 09). The call for papers attracted 68 submissions from Asia, Canada, Europe, Africa, and the United States. The program committee accepted 20 papers that cover a variety of topics, including Grid middleware and distributed algorithms, resource management and scheduling, data management, parallel algorithms and applications, workflow and dataflow applications, I/O and parallel computing. We hope that these proceedings will serve as a valuable reference for researchers and developers.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.131 | 0.087 |
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".