[IPDPS] MPP 2021: 10th Workshop on Parallel Programming Models - Emerging Technologies on Machine Learning Acceleration

[Leandro Santiago de Araújo]

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MPP 2021: 10th Workshop on Parallel Programming Models
co-conducted with IPDPS 2021 on May 21st, 2021, at Portland, Oregon USA.
http://www.mpp-conf.org/
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Current trends in Computer Architecture and Parallel Programming point
towards the importance of accelerating Machine Learning (ML) Algorithms.
The ubiquity of ML, the amount of data treated by ML and the adoption of
much more complex Artificial Neural Networks (mostly Deep Neural Networks
(DNNs)) reinforce the importance of tackling ML problems from a performance
and energy efficiency point-of-views. Therefore, this issue of the Workshop
on Parallel Programming Models (MPP) will mainly focus on works that
provide acceleration and energy efficiency to ML systems, but as usual will
also welcome papers in any topic related to parallelism/acceleration.

MPP is a workshop designed to explore parallel programming models,
architectures, and runtime systems to enable developers to deal with these
trade-offs. MPP has been held each year since 2012, co-locating with
prestigious conferences such as WSCAD, SBAD-PAC, and IPDPS. It has
attracted industry sponsorship (Maxeler, LG, Microsoft, NGD Systems) and
top-tier keynotes, such as Arvind - MIT, Michael Flynn - Stanford and Jesus
Labarta - BSC. MPP 2021 will be focused on Machine Learning Performance and
has the potential of attracting high-quality papers and audience for
fruitful discussions.

When addressing the performance aspect of Machine Learning, there is also
the issue of the amount of data used for training deep-learning models. In
the case of Big Data, the application of in-memory computing (which was the
main topic in MPP 2019) can be essential to reduce the gap between data and
the ML model, in terms of latency.

MPP 2021 aims at bringing together researchers interested in presenting
contributions to the evolution of existing models or in proposing novel
ones, considering the trends on Machine Learning, In-Memory Computing and
Security. *MPP 2021 will be held in conjunction with The 35th IEEE
International Parallel and Distributed Processing Symposium (IPDPS 2021),
in *Hilton Portland Downtown, Portland, Oregon, United States, on Friday,
May 21.


*Submission Guidelines*

MPP invites authors to submit unpublished full (8 pages maximum) or short
(4 pages maximum) papers on the subject. Submitted manuscripts must be
single-spaced double-column pages using 10-point size font on 8.5x11 inch
pages (IEEE conference style), including figures, tables, and references.
The submitted manuscripts should include author names and affiliations. Papers
must be submitted by Feb, 18, 2020, in the following url:
https://easychair.org/conferences/?conf=mpp2021


*List of Topics*

Topics of interest include (but are not limited to):

   - Compression of Deep-Learning Models;
   - Tools for ML Model design;
   - Hardware specifically designed for Machine Learning;
   - In-Memory Computing;
   - Novel Deep Neural Networks architectures;
   - Error Detection/Recovery in ML systems;
   - Robust Neural Networks;
   - Privacy of data in ML systems;
   - Robustness of decision making ML systems;
   - Neural networks inference and training on IoT, Fog, Edge and cloud
   environments;
   - Machine Learning for Parallel Applications and IoT.


The proceedings of MPP 2021 will be distributed at IPDPS 2021 and will be
submitted for inclusion in the IEEE Xplore after the conference.


*Important Dates:*

   - Paper submission deadline:
      - Abstract: *February 17, 2021*
      - Paper: *February 21, 2021*
   - Author notification: *March 10**, 2021*
   - Camera-ready: *****March 15, 2021*
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