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EBDMA'17 - Workshop on the Integration of Extreme Scale Computing and Big Data Management and Analytics
http://bigstorage-project.eu/
Co-located with IEEE/ACM CCGrid 2017
May 14-17, 2017 - Madrid, Spain
https://www.arcos.inf.uc3m.es/
------------------------------
WORKSHOP DESCRIPTION
The deployment of extreme scale computing platforms in research and industry coupled with the proliferation of large and distributed digital data sources have the potential for unprecedented insights and understanding in all areas of science, engineering, business, and society in general. However challenges related to the Big Data generated and processed by these systems remain a significant barrier in achieving this potential.
Addressing these challenges requires a seamless integration of the extreme scale/high performance computing, cloud computing, storage technologies, data management, energy efficiency, and big data analytics research approaches, framework/technologies, and communities. The convergence and integration of HPC, cloud computing and data analysis is crucial to the future. To achieve this goal, both communities need to collectively explore and embrace emerging disruptions in architecture and hardware technologies as well as new data-driven application areas such as those enabled by the Internet of Things. Finally, educational and workforce development structures have to evolved to develop the required integrated skillsets.
The goal of this workshop is to bring leading researchers from these communities together to jointly explore such integration, and to develop a research agenda towards brings the diverging research groups and technologies stack toward a more convergent path. The workshop provides a forum for scientists and engineers in academia and industry to present their latest research findings on major and emerging topics in this field.
TOPICS
Topics of interest include, but are not limited to:
- Models and techniques for scalable data analysis
- Extreme data discovery solutions
- HPC and extreme scale platforms for Big Data analytics
- Exascale data analysis programming abstractions and services
- Parallel and distributed Big Data analysis algorithms
- Data analysis as a service infrastructure
- Code coordination and data integration on HPC platforms
- Interoperability of Big Data analytics frameworks
- Adaption of data mining algorithms on extreme scale systems
- Data-centric scalable programming tools and algorithms
- High-performance and Big Data analytics frameworks, programming models, and tools
- Leveraging processing, storage and communications technologies (multi/many-core architectures, accelerators, RDMA-enabled networking, NVRAMs and SSDs) in integrated HPC Big Data applications
- Performance modeling and evaluation of integrated HPC Big Data applications
- Fault tolerance, reliability and availability for high-performance Big Data computing
- New storage devices for Big Data management in HPC and Clouds
- Security issues in Big Data analysis and management in HPC and Clouds
- Energy-efficiency issues in Big Data analysis and management in HPC and Clouds
- Stream data processing in HPC and Clouds
- Case studies of data-intensive applications in HPC and Clouds
- Scheduling and provisioning data analytics on hybrid Cloud and HPC infrastructure
SUBMISSION AND PUBLICATION
Papers should be formatted according to the IEEE Proceedings format and be submitted in PDF through the CCGrid 2017 conference website (https://www.easychair.org/
All papers accepted and presented at the EBDMA 2017 workshop will be published in the IEEE/ACM CCGrid conference proceedings, and will be submitted to IEEE Xplore for publication and EI indexing.
IMPORTANT DATES
Paper submission: January 16, 2017 (extended)
Notification of acceptance: February 10, 2017
Camera-ready version: February 24, 2017
WORKSHOP CO-CHAIRS
Shadi Ibrahim, Inria, France
Manish Parashar, Rutgers University, USA
Anna Queralt, Barcelona Supercomputing Center, Spain
Domenico Talia, University of Calabria, Italy
PROGRAM COMMITTEE
Ilkay Altintas, University of California, San Diego, USA
Andre Brinkmann, Johannes Gutenberg-Universität Mainz, Germany
Gene Cooperman, Northeastern University, USA
Alexandru Costan, Inria Rennes, France
Frederic Desprez, Inria, France
Simon Dobson, University of St Andrews, UK
Jack Dongarra, University of Tennessee, USA
Matthieu Dorier, Argonne National Laboratory, USA
Bingsheng He, National University of Singapore, Singapore
Hai Jin, Huazhong University of Science and Technology, China
Scott Klasky, Oak Ridge National Laboratory, USA
Dieter Kranzlmueller, Ludwig-Maximilians-
Michael Kuhn, University of Hamburg, Germany
Adrien Lebre, Inria Ecole des Mines, France
Laurent Lefevre, Inria, France
Manolis Marazakis, Instutute of Computer Science, FORTH, Greece
Ramón Nou, Barcelona Supercomputing Center, Spain
Anne-Cécile Orgerie, Centre National de la Recherche Scientifique, France
Dana Pectu, West University of Timisoara, Romania
Maria Perez, Universidad Politecnica de Madrid, Spain
Depei Qian, Beihang University, China
Rob Ross, Argonne National Laboratory, USA
Paolo Trunfio, University of Calabria, Italy
Vladimir Vlassov, KTH Royal Institute of Technology, Sweden
Amelie Chi Zhou, Inria Rennes, France
http://bigstorage-project.eu/
Co-located with IEEE/ACM CCGrid 2017
May 14-17, 2017 - Madrid, Spain
https://www.arcos.inf.uc3m.es/
------------------------------
WORKSHOP DESCRIPTION
The deployment of extreme scale computing platforms in research and industry coupled with the proliferation of large and distributed digital data sources have the potential for unprecedented insights and understanding in all areas of science, engineering, business, and society in general. However challenges related to the Big Data generated and processed by these systems remain a significant barrier in achieving this potential.
Addressing these challenges requires a seamless integration of the extreme scale/high performance computing, cloud computing, storage technologies, data management, energy efficiency, and big data analytics research approaches, framework/technologies, and communities. The convergence and integration of HPC, cloud computing and data analysis is crucial to the future. To achieve this goal, both communities need to collectively explore and embrace emerging disruptions in architecture and hardware technologies as well as new data-driven application areas such as those enabled by the Internet of Things. Finally, educational and workforce development structures have to evolved to develop the required integrated skillsets.
The goal of this workshop is to bring leading researchers from these communities together to jointly explore such integration, and to develop a research agenda towards brings the diverging research groups and technologies stack toward a more convergent path. The workshop provides a forum for scientists and engineers in academia and industry to present their latest research findings on major and emerging topics in this field.
TOPICS
Topics of interest include, but are not limited to:
- Models and techniques for scalable data analysis
- Extreme data discovery solutions
- HPC and extreme scale platforms for Big Data analytics
- Exascale data analysis programming abstractions and services
- Parallel and distributed Big Data analysis algorithms
- Data analysis as a service infrastructure
- Code coordination and data integration on HPC platforms
- Interoperability of Big Data analytics frameworks
- Adaption of data mining algorithms on extreme scale systems
- Data-centric scalable programming tools and algorithms
- High-performance and Big Data analytics frameworks, programming models, and tools
- Leveraging processing, storage and communications technologies (multi/many-core architectures, accelerators, RDMA-enabled networking, NVRAMs and SSDs) in integrated HPC Big Data applications
- Performance modeling and evaluation of integrated HPC Big Data applications
- Fault tolerance, reliability and availability for high-performance Big Data computing
- New storage devices for Big Data management in HPC and Clouds
- Security issues in Big Data analysis and management in HPC and Clouds
- Energy-efficiency issues in Big Data analysis and management in HPC and Clouds
- Stream data processing in HPC and Clouds
- Case studies of data-intensive applications in HPC and Clouds
- Scheduling and provisioning data analytics on hybrid Cloud and HPC infrastructure
SUBMISSION AND PUBLICATION
Papers should be formatted according to the IEEE Proceedings format and be submitted in PDF through the CCGrid 2017 conference website (https://www.easychair.org/
All papers accepted and presented at the EBDMA 2017 workshop will be published in the IEEE/ACM CCGrid conference proceedings, and will be submitted to IEEE Xplore for publication and EI indexing.
IMPORTANT DATES
Paper submission: January 16, 2017 (extended)
Notification of acceptance: February 10, 2017
Camera-ready version: February 24, 2017
WORKSHOP CO-CHAIRS
Shadi Ibrahim, Inria, France
Manish Parashar, Rutgers University, USA
Anna Queralt, Barcelona Supercomputing Center, Spain
Domenico Talia, University of Calabria, Italy
PROGRAM COMMITTEE
Ilkay Altintas, University of California, San Diego, USA
Andre Brinkmann, Johannes Gutenberg-Universität Mainz, Germany
Gene Cooperman, Northeastern University, USA
Alexandru Costan, Inria Rennes, France
Frederic Desprez, Inria, France
Simon Dobson, University of St Andrews, UK
Jack Dongarra, University of Tennessee, USA
Matthieu Dorier, Argonne National Laboratory, USA
Bingsheng He, National University of Singapore, Singapore
Hai Jin, Huazhong University of Science and Technology, China
Scott Klasky, Oak Ridge National Laboratory, USA
Dieter Kranzlmueller, Ludwig-Maximilians-
Michael Kuhn, University of Hamburg, Germany
Adrien Lebre, Inria Ecole des Mines, France
Laurent Lefevre, Inria, France
Manolis Marazakis, Instutute of Computer Science, FORTH, Greece
Ramón Nou, Barcelona Supercomputing Center, Spain
Anne-Cécile Orgerie, Centre National de la Recherche Scientifique, France
Dana Pectu, West University of Timisoara, Romania
Maria Perez, Universidad Politecnica de Madrid, Spain
Depei Qian, Beihang University, China
Rob Ross, Argonne National Laboratory, USA
Paolo Trunfio, University of Calabria, Italy
Vladimir Vlassov, KTH Royal Institute of Technology, Sweden
Amelie Chi Zhou, Inria Rennes, France
******************************
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If you do not remember your password (which is needed to change these options), you can reset it using the "Unsubscribe or Edit Options" button at the bottom of the page.
(https://lists.mcs.anl.gov/
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