HPML 2018

High Performance Machine Learning Workshop

News: Program released

September 24, 2018, Lyon, France.

Held in conjunction with IEEE SBAC-PAD 2018


This workshop is intended to bring together the Machine Learning (ML), Artificial Intelligence (AI) and High Performance Computing (HPC) communities. In recent years, much progress has been made in Machine Learning and Artificial Intelligence in general. This progress required heavy use of high performance computers and accelerators. Moreover, ML and AI have become a “killer application” for HPC and, consequently, driven much research in this area. These facts point to an important cross-fertilization that this workshop intends to nourish.

We invite researchers and professionals to take part in this workshop to discuss the challenges of Machine Learning, AI and HPC, and share their insights, use cases, tools and best practices.

HPML is held in conjunction with the 30th edition of SBAC-PAD. SBAC-PAD is an international conference on High Performance Computing started in 1987 in which scientists and researchers present recent findings in the fields of parallel processing, distributed computing and computer architecture.

Proceedings will be published in IEEE Xplore. Furthermore, a number of selected papers will be invited to a special issue of the Journal of Parallel and Distributed Computing (Elsevier - JPDC):

ELSEVIER JPDC - Special Issue on Advances on Parallel and High Performance Computing for AI Applications

HPML 2018 flyer


Keynote speech:

Title: Serendipity: How supercomputing technology is enabling a revolution in artificial intelligence

Speaker: José E. Moreira, Distinguished Research Staff Member, IBM Research

Abstract: With the availability of both large compute power and large data sets, we have witnessed a revolution in machine learning technology, which has become a mainstream tool for both business and scientific applications. This revolution is likely to accelerate, as even more compute power is brought to bear, and deliver many of the promises of artificial intelligence. In this talk we will investigate how far the impacts of machine learning can go. We will cover the new Summit supercomputer, which brings unprecedented compute capabilities to both traditional high performance computing and artificial intelligence problems, analyzing the similarities as well as the differences in those two fields. We will also speculate about the future of machine learning and, in particular, its possible limitations. We will conclude with a discussion of one of the most important scientific questions of our time: Is consciousness computable?

Bio: José E. Moreira IBM Research Division, Thomas J. Watson Research Center, Yorktown Heights, New York. Dr. Moreira is a Distinguished Research Staff Member in the Scalable Systems Department at the Thomas J. Watson Research Center. He received a B.S. degree in physics and B.S. and M.S. degrees in electrical engineering from the University of Sao Paulo, Brazil, in 1987, 1988 and 1990, respectively. He also received a Ph.D. degree in electrical engineering from the University of Illinois at Urbana-Champaign in 1995. Since joining IBM at the Thomas J. Watson Research Center, he has worked on a variety of high-performance computing projects. He was system software architect for the Blue Gene/L supercomputer and chief architect of the Commercial Scale Out project. He currently leads the IBM Research work on the architecture of Power processor. He is an author or coauthor of over 100 technical papers and 10 patents. Dr. Moreira is a member of the IEEE (Institute of Electrical and Electronics Engineers) and a Distinguished Scientist of the ACM (Association for Computing Machinery).



Monday, September 24

09:00 10:00

KEYNOTE - Serendipity: How Supercomputing Technology is Enabling a Revolution in Artificial Intelligence

José E. Moreira

IBM Research

10:00 10:30

Coffee & Tea & Juice Break


10:30 10:55

Large Scale Language Modeling: Converging on 40GB of Text in Four Hours

Raul Puri, Robert Kirby, Nikolai Yakovenko, Bryan Catanzaro

Nvidia USA


10:55 11:20

Accelerating deep neural network training for action recognition on a cluster of GPUs

Guojing Cong, Giacomo Domeniconi, Joshua Shapiro, Fan Zhou, Barry Chen

IBM TJ Watson, Georgia Tech, Lawrence Livermore National Laboratory


11:20 11:45

An argument in favor of strong scaling for deep neural networks with small datasets

Renato Cunha, Eduardo Rodrigues, Matheus Palhares Viana and Dario Augusto Borges Oliveira

IBM Research


11:45 12:10

Deep Learning on Large-scale Multicore Clusters

Kazumasa Sakiyama, Shinpei Kato, Yutaka Ishikawa, Atsushi Hori and Abraham Monrroy

The University of Tokyo, RIKEN, Nagoya University


12:10 12:35

On the Resilience of RTL NN Accelerators: Fault Characterization and Mitigation

Behzad Salami, Osman Unsal and Adrian Cristal Kestelman

Barcelona Supercomputing Center


12:35 13:00

t-SNE-CUDA: GPU-Accelerated t-SNE and its Applications to Modern Data

David Chan, Roshan Rao, Forrest Huang and John Canny

University of California, Berkeley


13:00 14:00

Lunch at the Buisson Cafeteria


14:00 14:25

HyperSpace: Distributed Bayesian Hyperparameter Optimization

M. Todd Young, Jacob Hinkle, Arvind Ramanathan, Ramakrishnan Kannan

Oak Ridge National Lab


14:25 14:50

A Machine Learning Approach for Parameter Screening in Earthquake Simulation

Marisol Monterrubio-Velasco, Jose Carlos Carrasco-Jiménez, Octavio Castillo-Reyes, Fernando Cucchietti, Josep De la Puente

Barcelona Supercomputing Center


14:50 15:15

A Case Study on Optimizing Accurate Half Precision Average

Kenny Peou, Joel Falcou and Alan Kelly

Numscale, Université Paris-Saclay


15:15 15:40

Optimization of a sparse grid-based data mining kernel for architectures using AVX-512

Paul Cristian Sârbu and Hans-Joachim Bungartz

Technical University of Munich


15:40 16:05

Energy Efficient Parallel K-Means Clustering for an Intel® hybrid Multi-Chip Package

Matheus A. Souza, Lucas A. Maciel, Pedro H. Penna and Henrique C. Freitas

PUC Minas


16:05 16:30

Coffee & Tea & Juice Break


16:30 16:55

Performance Comparison of a Parallel Recommender Algorithm across three Hadoop-based Frameworks

Christina Diedhiou, Bryan Carpenter, Aamir Shafi, Soumabha Sarkar, Ramazan Esmeli, Ryan Gadsdon

University of Portsmouth, Imam Abdulrahman Bin Faisal University


16:55 17:20

Effect Of Network Topology On The Performance Of ADMM-based SVMs

Shirin Tavara and Alexander Schliep

University of Boras, University of Gothenburg


17:20 17:45

High Performance Ensembles of Online Sequential Extreme Learning Machine for Regression and Time Series Forecasting

Luís F. L. Grim and André L. S. Gradvohl

University of Campinas


17:45 18:00

Closing remarks

Thursday, September 27

19:00 22:30

Best paper award announcement @ SBAC-PAD Banquet Reception on the Hermes boat 3 Hours Cruise along the Rhone and Saone Rivers



Topics of interest include, but are not limited to:

  • Machine learning (including deep learning) models
  • Large scale machine learning applications
  • Statistical models
  • Large scale data analytics
  • Machine learning applied to HPC
  • Accelerated Machine Learning
  • HPC applied to Machine Learning
  • Benchmarking, performance measurements, and analysis of ML models
  • Hardware acceleration for ML and AI
  • Parallel ML and AI models
  • HPC infrastructure and resource management for ML
  • Parallel Causal Models


We invite authors to submit original work to HPML. All papers will be peer reviewed and accepted papers will be published in IEEE Xplore. Furthermore, a number of selected papers will be invited to a special issue of the Journal of Parallel and Distributed Computing (JPDC) - Elsevier.

Submissions must be in English, limited to 8 pages in the IEEE conference format (see https://www.ieee.org/conferences/publishing/templates.html )

All submissions should be made electronically through the Easychair website ( https://easychair.org/conferences/?conf=hpml2018 ).

At least one author who has a paper accepted must register for the conference - IEEE SBAC-PAD 2018 - and attend the workshop to present their paper. Authors who do not register and present their paper, or arrange for a knowledgeable colleague to present it, will not have their paper published.


Organizing Committee

  • Eduardo Rocha Rodrigues, IBM Research - edrodri (at) br (dot) ibm (dot) com
  • Jairo Panetta, Instituto Tecnologico de Aeronautica, ITA, Brazil
  • Bruno Raffin, INRIA, France

Program Committee

  • Abhishek Gupta, Schlumberger, USA
  • Albert N. Kahira, Barcelona Supercomputing Center, Spain
  • Aline Paes, Universidade Federal Fluminense, UFF, Brazil
  • Andrea Schwertner Charao, Universidade Federal de Santa Maria, UFSM, Brazil
  • Bruno Silva, IBM Research, Brazil
  • Celso Mendes, Instituto Nacional de Pesquisas Espaciais, INPE, Brazil
  • Daniel Salles Chevitarese, IBM Research, Brazil
  • Dingwen Tao, University of Alabama, USA
  • Edmilson Morais, IBM Research, Brazil
  • Eduardo Vasconcellos, Universidade Federal Fluminense, UFF, Brazil
  • Emmanuel Jeannot, INRIA, France
  • Fabio Cozman, Universidade de Sao Paulo, USP, Brazil
  • Francis Birck Moreira, Universidade Federal do Rio Grande do Sul, UFRGS, Brazil
  • François Tessier, Argonne National Laboratory, USA
  • Gina Maira Barbosa de Oliveira, Universidade Federal de Uberlandia, UFU, Brazil
  • Guillaume Aupy, INRIA, France
  • Haroldo Fraga de Campos Velho, Instituto Nacional de Pesquisas Espaciais, INPE, Brazil
  • Ian Masliah, Laboratoire d’Informatique de Paris, LIP, France
  • Jose Celaya, Schlumberger, USA
  • Leonardo Bautista Gomez, Barcelona Supercomputing Center, Spain
  • Lucas Mello Schnorr, Universidade Federal do Rio Grande do Sul, UFRGS, Brazil
  • Luiz Gustavo Almeida Martins, Universidade Federal de Uberlandia, UFU, Brazil
  • Marc Casas, Barcelona Supercomputing Center, Spain
  • Marceli Zanon-Boito, Laboratoire d’Informatique de Grenoble, LIG, France
  • Marco Netto, IBM Research, Brazil
  • Mauricio Araya, Shell Oil USA, USA
  • Oguz Kaya, INRIA, France
  • Olivier Beaumont, INRIA, France
  • Olivier Coulaud, INRIA, France
  • Pedro Mario Cruz, Nvidia, Brazil
  • Renato Cunha, IBM Research, Brazil
  • Suhas Suresha, Schlumberger, USA
  • Xin Liang, University of California Riverside, USA

Important Dates

Abstract deadline: June 18th, 2018

Submission deadline: June 25th, 2018 July 9th, 2018

Acceptance notifications: July 20th, 2018

Camera-ready papers: July 31th, 2018

Workshop: September 24th, 2018

Best paper announcement: 27th, 2018 (@ SBAC-PAD Banquet Reception / 3 Hours Cruise along the Rhone and Saone Rivers)