A special issue from the international conference on performance engineering 2013
Bibliographic record
Abstract
The International Conference on Performance Engineering (ICPE) took place in Prague in the Czech Republic on April 21–24, 2013. The conference was attended by over 160 delegates. The main research track for ICPE attracted 42 submissions from which 20 full papers and two short papers were accepted for presentation. The Program Committee comprised over 50 leading academics and industrialists from 12 countries. There was also an Industry and Experience track that focused on the application of research results and performance-related technologies to industrial performance-engineering problems and the reporting of insightful performance results based on industry experience. This track received 22 submissions of which eight were selected for presentation. The papers accepted to these two main tracks covered a range of topics addressing performance-driven software development and various flavors of modeling, including performance, survivability, and scalability modeling. The development of representative workloads and benchmarks was also well represented. There were then a number of papers that focused on performance aspects of cloud-related systems and more general aspects of scheduling and load balancing. ICPE also had a Vision/Work-in-Progress track that provided a forum for researchers to present and discuss newly-emerging ideas and showcase ongoing work that shows promise. This track received 18 submissions of which 10 were accepted for short presentations at the conference. At the end of the conference, the authors of 11 papers from the Research Track and the Industry and Experience Track were invited to submit extended versions of their papers for consideration in this special issue. The extended papers submitted went through a new round of reviews, and this led to the selection of the six contributions that appear here. We would like to thank the authors of all the extended papers for their diligence in preparing their manuscripts for consideration and the members of the special issue Program Committee for their many insightful and helpful suggestions. The contributions include a new analytical model for estimating the survivability of a smart power grid 1; a method for identifying (‘blaming’) software components and/or component compositions that result in performance errors in a component-based application 2; a new performance model for Hadoop's MapReduce framework that is based on modeling separately the generic and application-specific phases of the framework 3; a measurement-based study of the performance of web servers running on multi-core systems that can be used to understand the scaling behavior of a web server and to help find server configurations that optimize performance for a given workload 4; a method for dynamically switching between different time-series-based forecasting methods for an evolving workload trace whose behavior is controlled solely by user-defined forecasting objectives 5; a resampling technique that constructs a workload model from a supercomputer job trace that can then be used to conduct a simulation-based analysis of the performance of a parallel job scheduler 6. This rich diversity of research contributions to performance engineering reflects the evolution of the field and the roots of ICPE, which evolved from a performance evaluation workshop sponsored by the Standard Performance Evaluation Corporation (SPEC) and from the Workshop on Software and Performance (WOSP). While the SPEC workshop focused on benchmarking and encompassed many areas of software and hardware evaluation, the main focus of WOSP was the development of methodologies for software performance modeling, prediction, and engineering. Besides reflecting this diversity, the contributions in this special issue also point to the potential for fruitful collaborations and mutual learning between these two segments of the performance evaluation community. We are excited by the potential future opportunities that this union affords and hope that this special issue provides a useful insight into some of the current key topics.
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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 | 0.001 |
| Open science | 0.000 | 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".