Proceedings of the twenty-fifth annual ACM symposium on Parallelism in algorithms and architectures
Bibliographic record
Abstract
This volume consists of papers that were presented at the 25th ACM Symposium on Parallelism in Algorithms and Architectures (SPAA 2013), held on 23--25 July 2013, in Montreal, Canada, colocated with PODC. It was sponsored by the ACM Special Interest Groups on Algorithms and Computation Theory (SIGACT) and Computer Architecture (SIGARCH) and organized in cooperation with the European Association for Theoretical Computer Science (EATCS). Financial support was provided by Akamai, IBM Research, Sandia National Laboratories, Oracle Labs and ACM SIGARCH. The program committee selected 31 regular presentations following electronic discussions. Of these papers, the papers "IRIS: A Robust Information System Against Insider DoS-Attacks" by Martina Eikel and Christian Scheideler and "Fast Greedy Algorithms in MapReduce and Streaming" by Ravi Kumar, Benjamin Moseley, Sergei Vassilvitskii, and Andrea Vattani were selected to receive the best paper award. The regular presentations were selected out of 130 submitted manuscripts. The mix of selected papers reflects the unique nature of SPAA in bringing together the theory and practice of parallel computing. SPAA defines parallelism very broadly to encompass any computational device or scheme that can perform multiple operations or tasks simultaneously or concurrently. The technical papers in this volume are to be considered preliminary versions, and authors are generally expected to publish polished and complete versions in archival scientific journals. In addition to the regular presentations, this volume includes 8 brief announcements. The committee's decisions in accepting brief announcements were based on the perceived interest of these contributions, with the goal that they serve as bases for further significant advances in parallelism in computing. Extended versions of the SPAA brief announcements may be published later in other conferences or journals. Finally, this year's program also included the ACM Athena lecture given by Nancy Lynch of the Massachusetts Institute of Technology and additional keynote addresses by Marc Snir of Argonne National Laboratory and the University of Illinois at Urbana-Champaign and by Philipp Woelfel of the University of Calgary.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.113 | 0.053 |
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".