Connectionists: MPP 2019 -- 8th Workshop on Parallel Programming Models - Special Edition on IoT and Machine Learning

Wed Nov 21 08:36:54 EST 2018

MPP 2019 -- 8th Workshop on Parallel Programming Models - Special Edition
on IoT and Machine Learning

http://mpp-conf.org

In Conjunction with IPDPS 2019, Rio de Janeiro, Brazil

http://www.ipdps.org

Call for Papers

Recent trends in artificial neural networks, such as deep neural networks,
and the Internet-of-Things – IoT, indicate that an increasing number of
artificial intelligence -based applications will be running on smartphones,
sensors and other IoT devices collecting and processing large amounts of
data. Most of those devices have limited processing power and often rely on
cloud services for compute-intensive tasks. However, real-time applications
may not tolerate the latency of offloading tasks to a cloud server.

Another important aspect to consider, especially in applications that run
on big systems and manipulate big data sets, is the trade-off between
moving data to a remote processing element to increase parallelism and
computing things locally to reduce communication and energy costs while
keeping performance levels. Edge/Fog computing proposes bringing
computation closer to where data is sitting, by adding computational
capabilities to network devices and adding edge gateways/servers, possibly
in multiple layers with different latencies and computing performance.
Moreover, such systems are expected to be heterogeneous, including
multi-core processors, GPUs, FPGAs, and even processors that are customized
for certain applications.

In this scenario, writing parallel applications is a non-trivial task, but
also mandatory if one wants to explore the potential of the aforementioned
modern computing platforms, imposing new challenges to the scientific
community: the creation of models and alternatives to ease parallelism
exploitation by the average programmer, considering the peculiarities of
the different computation devices. Moreover, the proposed solutions should
tackle problems such as application deployment, resilience and
scheduling/offloading of tasks, considering latency, bandwidth, response
time and computing power. In these complex environments, Machine Learning
is becoming an important trend for the autonomic operation.

MPP aims at bringing together researchers interested in presenting
contributions to the evolution of existing models or in proposing novel
ones, considering the trends on IoT and Machine Learning. MPP 2019 will be
held in conjunction with The 33rd IEEE International Parallel and
Distributed Processing Symposium (IPDPS 2019), in Rio de Janeiro, Brazil on
May 20-24, 2019.

Submission Guidelines

MPP invites authors to submit unpublished full and short papers on the
subjects. Submissions must be in English, 8 pages maximum for full papers
and 4 pages for short papers, following the IEEE formatting guidelines.
Page limits include references.

List of Topics

Topics of interest include (with special emphasis  on IoT, Fog, Edge
Computing, and Machine Learning) :

   -

   Novel execution models and languages for parallelism;
   -

   Novel parallel programming techniques and architectures;
   -

   Heterogeneous programming models;
   -

   Synchronization mechanisms;
   -

   Storage techniques;
   -

   Load-balancing and scheduling mechanisms;
   -

   Error detection/recovery;
   -

   Theoretical analysis of systems;
   -

   Smart network devices;
   -

   Software-defined networks;
   -

   Integration of IoT, Fog, Edge and Cloud Computing;
   -

   Neural Networks inference and training on IoT, Fog, Edge and cloud
   environments;
   -

   Performance analysis; and
   -

   Applications.

Important Dates

   -

   Paper submission deadline: February 4, 2019
   -

   Author notification: February 25, 2019
   -

   Camera-ready: March 15, 2019

Publication

The proceedings of MPP will be submitted to IEEE Xplore and Computer
Society Digital Library.

MPP is currently seeking for a journal to publish selected papers after the
conference.

Venue

MPP 2018 will be co-held with IPDPS at Hilton Rio de Janeiro Copacabana,
Rio de Janeiro, Brazil.

More information will be available at the Conference website
http://www.ipdps.org/ .

Contact

All questions about submissions should be emailed to
mpp2019 at googlegroups.com .

General co-chairs

   -

   Leandro A. J. Marzulo - Google Research, USA
   -

   Felipe M. G. França - Universidade Federal do Rio de Janeiro (UFRJ),
    Brazil

Program co-chairs

   -

   Cristiana Bentes - Universidade do Estado do Rio de Janeiro (UERJ),
   Brazil
   -

   Gabriele Mencagli - University of Pisa, Italy

Steering co-chairs

   -

   Andrew Putnam - Microsoft Research, USA
   -

   Mauricio Pilla - Universidade Federal de Pelotas, Brazil

Steering Committee

   -

   Daniel Mosse  - University of Pittsburgh,  USA
   -

   Edson Borin - Universidade Estadual de Campinas (UNICAMP), Brazil
   -

   Lúcia Drummond - Universidade Federal Fluminense (UFF), Brazil
   -

   Mario Dantas - Universidade Federal de Santa Catarina (UFSC), Brazil
   -

   Nader Bagherzadeh - University of California Irvine, USA
   -

   Nelson Amaral - University of Alberta, USA
   -

   Rodolfo Azevedo - UNICAMP, Brazil
   -

   Sandip Kundu - University of Massachusetts Amherst, USA
   -

   Vladimir Alves - NGD Systems, USA

Program Committee

   -

   Albert Y. Zomaya - University of Sydney, Australia
   -

   Aletéia Araújo - Universidade de Brasília, Brazil
   -

   Alexandre da Costa Sena - Universidade do Estado do Rio de Janeiro
   (UERJ), Brazil
   -

   Alexandre Solon Nery - Universidade de Brasília (UnB), Brazil
   -

   Alexandre Sztajnberg - Universidade do Estado do Rio de Janeiro (UERJ),
   Brazil
   -

   Arthur Francisco Lorenzon - Universidade Federal do Pampa (UNIPAMPA),
   Brazil
   -

   Carla Osthoff - Laboratório Nacional de Computação Científica (LNCC),
   Brazil
   -

   Claudia Di Napoli - CNR, Italy
   -

   Claude Tadonki - Mines - ParisTech,  France
   -

   Cristina Boeres - Universidade Federal Fluminense (UFF), Brazil
   -

   Dalvan Griebler - Pontifícia Universidade Católica do Rio Grande do Sul,
   Brazil
   -

   Diego Dutra - Universidade Federal do Rio de Janeiro (UFRJ), Brazil
   -

   Edward Moreno - Universidade Federal do Sergipe, Brazil
   -

   Elias Mizan - Wave Computing, USA
   -

   Flavia Delicato - Universidade Federal do Rio de Janeiro (UFRJ), Brazil
   -

   Gabriel Paillard - Universidade Federal do Ceará (UFC), Brazil
   -

   Igor Machado Coelho - Universidade do Estado do Rio de Janeiro (UERJ),
   Brazil
   -

   Kazutomo Yoshii - Argonne National Laboratory, USA
   -

   Krommydas Konstantinos - Intel, USA
   -

   Luciana Arantes  - Université Paris 6 Pierre et Marie Curie,  France
   -

   Maria Clicia Stelling de Castro - Universidade do Estado do Rio de
   Janeiro (UERJ), Brazil
   -

   Mauricio Breternitz - Instituto Universitario de Lisboa, Portugal
   -

   Michael Frank - MagiCore Inc., USA
   -

   Rajesh Sankaran - Argonne National Laboratory, USA
   -

   Rafael dos Santos - ARM, United Kingdom
   -

   Rekai Gonzalez Alberquilla - ARM, UK
   -

   Roberto Souto - Laboratório Nacional de Computação Científica (LNCC),
   Brazil
   -

   Silvio Stanzani - Universidade Estadual Paulista (UNESP), Brazil
   -

   Tiago A. O. Alves – Universidade do Estado do Rio de Janeiro (UERJ),
   Brazil
   -

   Walid Najjar - University of California Riverside, USA
   -

   Wei Li - University of Sydney, Australia
   -

   Zehra Sura - IBM, USA

-- 
–––––––––––––––––––––––––––––––
Felipe M. G. França, PhD
Professor of Computer Science and Engineering
Systems Engineering and Computer Science Program, COPPE
Universidade Federal do Rio de Janeiro
P.O. Box 68511, 21941-972, Rio de Janeiro, RJ, Brazil
felipe at ieee.org
felipe at cos.ufrj.br
–––––––––––––––––––––––––––––––
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