Proceedings of the 1st ACM workshop on Large-Scale system and application performance
Bibliographic record
Abstract
It is our great pleasure to welcome you to the first Workshop on Large-scale System and Application Performance -- LSAP2009. We have initiated this workshop 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 large-scale systems and applications. The call for papers of the workshop attracted 7 submissions, out of which the program committee accepted 4 papers that cover a variety of topics, ranging from modular data centers and grid meta-brokers to the MapReduce programming model and peer-to-peer systems. In addition, the workshop program features a keynote presentation by Douglas Thain of the University of Notre Dame, USA, on scaling up data-intensive scientific applications.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".