[hpc-announce] [held online] CFP: Big Data & Deep Learning in HPC (IEEE Xplore) - Extended deadline: June 28, 2020

Carlos Ferreira cgf at isep.ipp.pt
Sat Jun 6 13:30:34 CDT 2020


in conjunction with the IEEE 32nd International Symposium on Computer Architecture and High
Performance Computing (SBAC-PAD 2020)

September 9, 2020, Porto, Portugal

** NEW ** BDL2020 will be held online (synchronous and/or asynchronous)

We are monitoring the Coronavirus disease (COVID-19) outbreak and
following the recommendations/guidelines from the World Health
Organization (WHO) and the European Centre for Disease Prevention and
Control (ECDC).

The safety of all conference participants is our main priority. In
this perspective, regardless of the outbreak outcomes in September, we
will make BDL2020 an online (synchronous and/or asynchronous)
event and we will maintain the regular publication activities, i.e.,
accepted papers will be eligible for publication at the IEEE Xplore.

The Workshop fee is now 200 euros.


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.


Deadline for paper submission: ***June 28, 2020***

Author notification: July 22, 2020

Camera-ready version of papers: July 25, 2020


We invite authors to submit original work to BDL. All papers will be peer reviewed and accepted papers
will be published in IEEE Xplore.

Submissions must be in English, limited to 8 pages in the IEEE conference format (see

All submissions should be made electronically through the EasyChair system:


A full registration to the workshop and presentation are needed in order to have your paper included
in the workshop proceedings.

The Workshop fee is 200 euros.

Registration system available in https://sbac2020.dcc.fc.up.pt/bdl2020/registration.html


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)
Miguel Areias (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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