[openstack-hpc] Final CFP: Big Data & Deep Learning in HPC (Springer LNCS)

Carlos Ferreira cgf at isep.ipp.pt
Thu Mar 24 18:35:52 UTC 2016


Porto, Portugal June 30, 2016

Workshop on Big Data & Deep Learning in High Performance Computing

in conjunction with VECPAR 2016 - High Performance Computing for Computational Science

Paper Submission Deadline: 27 March 2016



The number of very large data repositories (big data) is increasing in a rapid pace.
Analysis of such repositories using the "traditional" sequential implementations of ML
and emerging techniques, like deep learning, that model high-level abstractions in data
by using multiple processing layers, requires expensive computational resources and long
running times. Parallel or distributed computing are possible approaches that can make
analysis of very large repositories and exploration of high-level representations
feasible. Taking advantage of a parallel or a distributed execution of a ML/statistical
system may: i) increase its speed; ii) learn hidden representations; iii) search a larger
space and reach a better solution or; iv) increase the range of applications where it can
be used (because it can process more data, for example).  Parallel and distributed
computing is therefore of high importance to extract knowledge from massive amounts of
data and learn hidden representations.

The workshop will be concerned with the exchange of experience among academics, researchers
and the industry whose work in big data and deep learning require high performance
computing to achieve goals. Participants will present recently developed algorithms/systems,
on going work and applications taking advantage of such parallel or distributed environments.


All novel data-intensive computing techniques, data storage and integration schemes, and
algorithms for cutting-edge high performance computing architectures which targets Big Data
and Deep Learning are of interest to the workshop. Examples of topics include but not
limited to:
- parallel algorithms for data-intensive applications;
- scalable data and text mining and information retrieval;
- using Hadoop, MapReduce, Spark, Storm, Streaming to analyze Big Data;
- energy-efficient data-intensive computing;
- deep-learning with massive-scale datasets;
- querying and visualization of large network datasets;
- processing large-scale datasets on clusters of multicore and manycore processors, and accelerators;
- heterogeneous computing for Big Data architectures;
- Big Data in the Cloud;
- processing and analyzing high-resolution images using high-performance computing;
- using hybrid infrastructures for Big Data analysis.
- New algorithms for parallel/distributed execution of ML systems;
- applications of big data and deep learning to real-life problems.


Papers submitted to this workshop should not exceed 12 pages in length. The papers should be
formatted according to the rules of the Springer Series Lecture Notes in Computer Science (LNCS).

All accepted papers will be published by Springer in the series entitled Lecture Notes in
Computer Science. The proceedings will be distributed in electronic format to participants of VECPAR 2016.

You should make your submissions through the EasyChair system:


All participants must attend the VECPAR 2016 conference.


Carlos Ferreira (LIAAD - INESC TEC LA and Polytechnic Institute of Porto)
João Gama (LIAAD - INESC TEC LA and University of Porto)
Albert Bifet (Telecom ParisTech)
Vítor Santos Costa (CRACS - INESC TEC LA and University of Porto)
Rui Camacho (LIAAD -INESC TEC LA and University of Porto)

Carlos Ferreira

ISEP | Instituto Superior de Engenharia do Porto
Rua Dr. António Bernardino de Almeida, 431
4249-015 Porto - PORTUGAL
tel. +351 228 340 500 | fax +351 228 321 159
mail at isep.ipp.pt | www.isep.ipp.pt

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