[hpc-announce] Call for papers: MODA2020: 1st International Workshop on ?Monitoring and Operational Data Analytics? at ISC HPC, June 25, 2020

[Florina M. Ciorba]

*** CALL FOR PAPERS ***

1st ISC HPC International Workshop on “Monitoring and Operational
Data Analytics” (MODA)
June 25, 2020
https://moda20.sciencesconf.org/

1. Workshop scope
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The race to Exascale poses significant challenges for the collection and analysis of the vast amount of data that future HPC systems will produce, in terms of the increasing complexity of the machines, the scalability and intrusiveness of the adopted monitoring solution, and the interpretability and effective inference driven by the acquired data. The main scope of the 1st ISC-HPC International Workshop on Monitoring and Operational Data Analytics (MODA) is to provide insight into current trends in MODA, to identify potential gaps, and to offer an outlook into the future of the involved fields high performance-computing, databases, machine learning, and possible solutions for upcoming Exascale systems. Contributions matching the scope of the workshop will be related to:

1. Currently envisioned solutions and practices for monitoring systems at data centers and HPC sites.
Significant focus will be placed on operational data collection mechanisms respectively i) covering different system levels, from building infrastructure sensor data to CPU-core performance metrics, and ii) targeting different end-users, from system administrators to application developers and computational scientists.

2. Effective strategies for analyzing and interpreting the collected operational data.
Such strategies should particularly include (but are not limited to) different visualization approaches and machine learning-based techniques, potentially inferring knowledge of the system behavior and allowing for the realization of a proactive control loop.

This workshop is not targeting new solutions proposed in the context of application performance modeling and/or application performance analysis tools. Novel contributions in the area of compiler analysis, debugging, programming models and/or sustainability of scientific software are also considered out of the scope of the workshop.

MODA is becoming common practice at various international HPC sites. However, each site adopts a different, insular approach, rarely adopted in production environments and mostly limited to the visualisation of system and building infrastructure metrics for health check purposes. In this regard, we observe a gap between the collection of operational data and its meaningful and effective analysis and exploitation, which prevents the closing of the feedback loop between the monitored HPC system, its operation, and its end-users. Under these premises, the goals of the workshop can then be summarized in the following way:

1. Gather and share knowledge and establish a common ground within the international community with respect to best practices in monitoring and operational data analytics.
2. Discuss future strategies and alternatives for MODA, potentially improving existing solutions and envisioning a common baseline approach in HPC sites and data centers.
3. Establish a debate on the usefulness and applicability of AI techniques on collected operational data for optimizing the operation of production systems (e.g. for practices such as predictive maintenance, runtime optimization, optimal resource allocation and scheduling).

1.1 Topics of interest
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The contributions submitted to MODA will ideally address:
* State-of-the-practice method, tools, techniques in monitoring at various HPC sites
* Solutions for monitoring and analysis of operational data that work very well on large- to extreme-scale systems with a large number of users
* Solutions that have proven limitations in terms of efficiency of operational data collection  in real-time or in terms of the quality of the collected data
* Opportunities and challenges of using machine learning methods for efficient monitoring and analysis of operational data
* Integration of monitoring and analysis practices into production system software (energy and resource management) and runtime systems (scheduling and resource allocation)
* Discuss explicit gaps between operational data collection, processing, effective analysis, highly useful exploitation, and propose new approaches to closing these gaps for the benefit of improving HPC centre planning, operations, and research
* Other monitoring and operational data analysis challenges and approaches (data storage, visualization, integration into system software, adoption)

1.2 Submission and publication
========================
We will solicit original contributions in the form of original papers (6-12 pages) which will be peer-reviewed by the program committee members. All accepted papers will be presented during the workshop. We aim at a minimum of 4 and a maximum of 8 accepted papers, for the 8 x 30 minutes slots in the tentative workshop program (see Section 4).

We will publish the workshop papers together with the ISC 2020 proceedings, including an abstract of the keynote and invited talks, and a short white paper of the panel session.

High quality contributions may be considered for a full-length submission to a special journal issue in collaboration with ParCo, CPE, or other journals.

Papers should be submitted through the online system at https://moda20.sciencesconf.org, by navigating to the top right side of the page and creating an account by clicking on the downward arrow near the “Login” box.

===============
Deadline: March 1, 2020 (AoE)
Notification: April 6, 2020.
===============

2. Committee
===============

2.1 Workshop organizing committee

1. Florina Ciorba (florina.ciorba at unibas.ch<mailto:florina.ciorba at unibas.ch>) – University of Basel, Switzerland
2. Nicolas Lachiche (nicolas.lachiche at unistra.fr<mailto:nicolas.lachiche at unistra.fr>) - University of Strasbourg, France
3. Aurélien Cavelan (aurelien.cavelan at unibas.ch<mailto:aurelien.cavelan at unibas.ch>) - University of Basel, Switzerland
4. Daniele Tafani (Daniele.Tafani at lrz.de<mailto:Daniele.Tafani at lrz.de>) - Leibniz Supercomputing Centre, Germany
5. Utz-Uwe Haus (utz-uwe.haus at hpe.com<mailto:utz-uwe.haus at hpe.com>) - HPE EMEA Research Lab, Switzerland


2.2 Technical program committee

1. Andrea Bartolini (a.bartolini at unibo.it<mailto:a.bartolini at unibo.it>) - University of Bologna, Italy
2. Valeria Bartsch (valeria.bartsch at itwm.fraunhofer.de<mailto:valeria.bartsch at itwm.fraunhofer.de>) - Fraunhofer ITWM Kaiserslautern, Germany
3. Norm Bourassa (njbourassa at lbl.gov<mailto:njbourassa at lbl.gov>) - NERSC LBNL, USA
4. Jim Brandt (brandt at sandia.gov<mailto:brandt at sandia.gov>) - Sandia National Labs, USA
5. Rubén Cabezón (ruben.cabezon at unibas.ch<mailto:ruben.cabezon at unibas.ch>) - sciCORE, University of Basel, Switzerland
6. Carlo Cavazzoni (c.cavazzoni at cineca.it<mailto:c.cavazzoni at cineca.it>) - CINECA, Italy
7. Todd Gamblin (tgamblin at llnl.gov<mailto:tgamblin at llnl.gov>) - LLNL, USA
8. Thomas Ilsche (thomas.ilsche at tu-dresden.de<mailto:thomas.ilsche at tu-dresden.de>) - Technische Universität Dresden, Germany
9. Jacques-Charles Lafoucriere (Jacques-Charles.LAFOUCRIERE at CEA.FR<mailto:Jacques-Charles.LAFOUCRIERE at CEA.FR>) -- CEA, France
10. Erwin Laure (erwinl at pdc.kth.se<mailto:erwinl at pdc.kth.se>) - KTH, Sweden
11. Fiilippo Mantovani (filippo.mantovani at bsc.es<mailto:filippo.mantovani at bsc.es>) - BSC, Spain
12. Ariel Oleksia (ariel at man.poznan.pl<mailto:ariel at man.poznan.pl>) - Poznan Supercomputing Center, Poland
13. Melissa Romanus (MRomanus at lbl.gov<mailto:MRomanus at lbl.gov>) - NERSC LBNL, USA
14. Karthee Sivalingam (karthee.sivalingam at hpe.com<mailto:karthee.sivalingam at hpe.com>) - HPE EMEA Research Lab, UK
15. Heiko Schuldt (heiko.schuldt at unibas.ch<mailto:heiko.schuldt at unibas.ch>) - University of Basel, Switzerland
16. Martin Schulz (schulzm at in.tum.de<mailto:schulzm at in.tum.de>) - TU Munich / Leibniz Supercomputing Centre, Garching, Germany
17. Keiji Yamamoto (keiji.yamamoto at riken.jp<mailto:keiji.yamamoto at riken.jp>) - RIKEN, Japan