Proceedings of the third international workshop on Large-scale system and application performance
Bibliographic record
Abstract
It is our great pleasure to welcome you to the third Workshop on Large-scale System and Application Performance -- LSAP2011. We initiated this workshop two years ago as we think it addresses a very important and timely subject. Over the last decade, computer systems and applications in everyday use have grown to unprecedented scales. Large clusters serving millions of search requests per day, grids executing large workflows and parameter sweeps consisting of thousands of jobs, and supercomputers running complex e-science applications, have now hundreds of thousands of processing cores. In addition, clouds are quickly emerging as a large-scale computing infrastructure. Peer-to-peer systems and centralized video distribution systems that dominate the internet and complicated internet applications such as massive multiplayer online games are used by millions of people every day. In view of this tremendous growth, understanding the performance of large-scale computer systems and applications has become vital to institutional, commercial, and private interests. This workshop intends to be a venue for papers on performance evaluation methods, tools, and studies focusing on the challenges of large scale, such as decentralization, predictable performance, reliability, and scalability. It aims to bring together system designers and researchers involved with the modeling and performance evaluation of largescale systems and applications. The call for papers of the workshop attracted 8 submissions, out of which the program committee accepted 5 papers that cover a variety of topics, ranging from lock thrashing in multicore systems and file systems for exascale computing, to the visual analysis of I/O system behavior, the multi-scale analysis of large distributed systems, and the analysis of social gaming networks. In addition, the workshop program features a keynote presentation by Marc Snir of the University of Illinois at Urbana-Champaign, USA, on performance engineering for petascale systems and beyond.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.067 | 0.033 |
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".