[FOSDEM] [devroom-managers] [ANN] HPC, Big Data, and Data Science Devroom CfP

Vasiliki Kalavri vasia at apache.org
Wed Oct 19 12:38:56 CEST 2016

Hello everyone,

please find below the FOSDEM'17 HPC, Big Data, and Data Science Devroom
Call for Participation.



FOSDEM'17 HPC, Big Data, and Data Science Devroom Call for Participation
Saturday, February 4, 2017, Brussels, Belgium

Also available here: <https://hpc-bigdata-fosdem17.github.io>

# Overview

We are proud to announce the 2nd edition of the HPC, Big Data and Data
devroom, co-located with FOSDEM 2017. FOSDEM is an annual conference about
and open source software, attended by over 5000 developers and open-source
enthusiasts from all over the world. This devroom is organised by
from the HPC and Big Data communities, who are joining forces to bring both
communities together.

    High Performance Computing (HPC) and Big Data are two important
    to scientific computing. HPC typically deals with smaller, highly
    data sets and huge amounts of computation while Big Data, not
    deals with gigantic, unstructured data sets and focuses on the I/O
    bottlenecks. With the Big Data trend unlocking access to an
    amount of data, Data Science has emerged to tackle the problem of
    processes and approaches to extracting knowledge or insights from these
    sets. Machine learning and predictive analytics algorithms have joined
    family of more traditional HPC algorithms and are pushing the
    of cluster and data scalability.

    Free and Open Source communities have been the foundation of the HPC
and Big
    Data communities for some time. In the HPC community, it should be no
    surprise that 497 of the Top500 supercomputers in the world run Linux.
    the Big Data side, the Hadoop ecosystem has had a tremendous amount of
    Source contributions from a wide range of organizations coming together
    under the Apache Software Foundation.

    Our goal is to bring the communities together, share expertise, learn
how we
    can benefit from each other’s work and foster further joint research and
    collaboration. We welcome talks about Free and Open Source solutions to
    challenges presented by large scale computing, data management and data

The devroom will take place on Saturday, February 4th, 2017, at ULB (Campus
Solbosch), in Brussels, Belgium. Join us to enjoy a full day of talks,
demos and
interesting discussions on open-source HPC, Big Data and Data Science.

Sounds interesting? Submit your talk proposal below and see you in Brussels!

# Topics

Topics of interest include, but are not limited to:

    Architecture and design of High Performance Computing (HPC) and Big Data

    Architecture and design of Extract, Transform and Load (ETL) and data
    acquisition pipelines

    Data security and governance

    Tools and technologies related to HPC and computational science, for
        Multithreading (OpenMP, etc.)
        Distributed computing (MPI, etc.)
        GPGPU computing (OpenCL, OpenACC, etc.)
        Parallel filesystems and storage
        Large-scale performance analysis and debugging
    Computational paradigms for Big Data systems
        MapReduce engines
        Streaming engines
        SQL engines
        Dataflow engines
    Emerging hardware trends of large scale clusters
        Large scale memory pooling
        High-speed interconnects
        ARM cluster architecture
    System administration of HPC and Big Data clusters

    User support tools

    Machine learning libraries and tools

    Scientific software applications, tools and libraries (across all

    Big Data platforms, extensions to existing systems, libraries, APIs

    Experience reports on using Big Data systems, for example:
        Large-scale deployments
        Development and configuration issues
        Tuning and performance tips and lessons learned
    Interesting Big Data use-cases and applications

    Comparative analysis of existing systems, evaluation results,

    Interdisciplinary HPC/Big Data use-cases, for example:
        Applications using both HPC and Big Data technologies
        Integration issues
        Open research problems on the convergence of HPC and Big Data
        Running MPI jobs on Big Data clusters and vice-versa

# Submission

We invite presenters to submit talk proposals to present high-quality work
sufficient background material to be clear to the HPC, Big Data, and/or Data
Science communities. Talk proposals should be submitted through the FOSDEM
Pentabarf server. Submissions must include:

    Session type
    Session length
    Expected prior knowledge / intended audience
    Speaker bio
    Links to code / slides / material for the talk (optional)
    Links to previous talks by the speaker

Our intention is to have a full day of talks of about 20 minutes each, with
additional 5 minutes for questions by attendees.

We would also like to note:

    Talks will be streamed live and will be recorded. By submitting a
    speakers agree to being recorded and having their talk made available.

    All accepted talks will be about (using) free and open source software.
    highly discourage “marketing” talks.

Submit here: <https://penta.fosdem.org/submission/FOSDEM17>

When submitting your talk in Pentabarf, make sure to select the ‘HPC, Big
and Data Science Devroom’ as the ‘Track’.

If you already have a Pentabarf account from a previous FOSDEM edition,
reuse it. Create an account if, and only if, you don’t have one from a
year. If you have any issues with Pentabarf, do not despair: contact
hpc-bigdata-devroom [at] lists.fosdem.org.

# Dates

Call for participation available: Wednesday 19 October 2016

Call for participation closes: *Friday 2 December 2016* (there will be no
extensions to the deadline!)

Devroom schedule available: 16 December 2016

Devroom date: Saturday 4 February 2017

# Organizers

    Ewan Higgs - Western Digital
    Kenneth Hoste - HPC team at Ghent University
    Roman Shaposhnik - Pivotal
    Vasia Kalavri - KTH Royal Institute of Technology (KTH)

Please email hpc-bigdata-devroom at lists.fosdem.org with questions or
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