[hpc-announce] Call for Contributions for the Data Challenge: The 14th ACM/SPEC International Conference on Performance Engineering (ICPE 2023)
Valeria Cardellini
cardellini at ing.uniroma2.it
Sun Nov 6 03:20:35 CST 2022
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Call for Contributions for the Data Challenge
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ICPE 2023
14th ACM/SPEC International Conference on Performance Engineering
Sponsored by ACM SIGMETRICS, SIGSOFT, and SPEC RG
April 15 - 19, 2023
Coimbra, Portugal
Web: https://icpe2023.spec.org
Twitter: https://twitter.com/ICPEconf
Submission: https://easychair.org/conferences/?conf=icpe2023
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IMPORTANT DATES
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Data challenge submission: Jan 15, 2023 (AoE)
Notification to the authors: Feb 24, 2023 (AoE)
SCOPE AND TOPICS
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Data is the foundation of many important decision-making processes in
performance engineering tasks of modern systems. Data can tell us about
the past and present of a system's performance, helping us predict
performance or assess the quality of our systems. In ICPE 2023, we will
continue to host a data challenge track in its second installment.
In this track, we provide a novel performance dataset from open source
Java systems collected by Traini et al. and published recently in the
Empirical Software Engineering journal. Participants are invited to come up
with new research questions about the dataset and study those. The challenge
is open-ended: participants can choose the research questions they find most
interesting. The proposed approaches and/or tools and their findings are
discussed in short papers and presented in the main conference.
How to participate in the challenge:
- Read the data description
- Think of something cool to do with the data. This can be anything you
want, including visualization, analysis, approach or tool
- Implement your idea, evaluate it, and write down your idea and the
results in a short paper
For more information, including the submission guidelines, visit:
https://icpe2023.spec.org/tracks-and-submissions/data-challenge-track/
Data description
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This year, the challenge dataset is provided by Traini et al., published
alongside their recent study "Towards effective assessment of steady state
performance in Java software: Are we there yet?"
(https://doi.org/10.48550/arXiv.2209.15369).
The dataset (https://github.com/SEALABQualityGroup/icpe-data-challenge-jmh)
contains a comprehensive set of performance measurements of 586 microbenchmarks
from 30 popular Java open source projects (e.g., RxJava, Log4J2, Apache Hive)
spanning various project domains (e.g., application servers, libraries,
databases). Microbenchmarks are frequently employed by practitioners to test
and ensure the adequate performance of their systems. Microbenchmark measurements
help open source maintainers test performance before landing new system features,
and identify performance regressions and optimization opportunities. Each
benchmark was carefully executed using the Java Microbenchmark Harness (JMH)
framework in a controlled environment to reduce measurement noise: results
contain, for each benchmark, 3000 measurements batches (JMH iterations) with
a minimum execution time of 100ms, repeated in 10 runs. This amounts to
more than 9 billion benchmark invocations for the entire dataset, an experiment
that lasted ~93 days.
ORGANIZING COMMITTEE
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General Chairs
- Marco Vieira, University of Coimbra, Portugal
- Valeria Cardellini, University of Rome Tor Vergata, Italy
Data Challenge Track Chairs
- Diego Costa, The University of Quebec in Montreal, Canada
- Michele Tucci, Charles University, Czech Republic
Publicity & Social-Media Chairs
- Naghmeh Ivaki, University of Coimbra, Portugal
- Gabriele Russo Russo, University of Rome Tor Vergata, Italy
Finance Chair
- Nuno Laranjeiro, University of Coimbra, Portugal
Publications Chairs
- Daniel Sadoc Menasche, Federal University of Rio de Janeiro, Brazil
- Andrea Marin, Ca Foscari University of Venice, Italy
Web Chair
- José D’Abruzzo Pereira, University of Coimbra, Portugal
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