pdsw-DISCS 2017:

2nd Joint International Workshop on Parallel Data Storage & Data Intensive Scalable Computing Systems


HELD IN CONJUNCTION WITH SC17: THE INTERNATIONAL CONFERENCE
FOR HIGH PERFORMANCE COMPUTING, NETWORKING, STORAGE AND ANALYSIS.

Monday, November 13, 2017
Denver, CO

TiME: 9:00am - 6:00 pm

Location: TBA
SC WORkshop page - coming soon


Program Co-Chairs:

Lawrence Livermore National Laboratory


Google
General Chair:

IBM

abstract / agenda / keynote speaker / cfp / submissions / WIP session / committees

agenda

Information on scheduling will be added here as the event approaches.


WORKSHOP ABSTRACT


(Find the complete proposal outlining the merger between PDSW and DISCS here.)

We are pleased to announce that the first Joint International Workshop on Parallel Data Storage and Data Intensive Scalable Computing Systems (PDSW-DISCS’16) will be hosted at SC16: The International Conference for High Performance Computing, Networking, Storage and Analysis.  The objective of this one day joint workshop is to combine two overlapping communities and to better promote and stimulate researchers’ interactions to address some of the most critical challenges for scientific data storage, management, devices, and processing infrastructure for both traditional compute intensive simulations and data-intensive high performance computing solutions.  Special attention will be given to issues in which community collaboration can be crucial for problem identification, workload capture, solution interoperability, standards with community buy­-in, and shared tools.

Many scientific problem domains continue to be extremely data intensive. Traditional high performance computing (HPC) systems and the programming models for using them such as MPI were designed from a compute-centric perspective with an emphasis on achieving high floating point computation rates. But processing, memory, and storage technologies have not kept pace and there is a widening performance gap between computation and the data management infrastructure. Hence data management has become the performance bottleneck for a significant number of applications targeting HPC systems.  Concurrently, there are increasing challenges in meeting the growing demand for analyzing experimental and observational data.  In many cases, this is leading new communities to look towards HPC platforms.  In addition, the broader computing space has seen a revolution in new tools and frameworks to support Big Data analysis and machine learning.  

There is a growing need for convergence between these two worlds.  Consequently, the U.S. Congressional Office of Management and Budget has informed the U.S. Department of Energy that new machines beyond the first exascale machines must address both the traditional simulation workloads as well as data intensive applications. This coming convergence prompts integrating these two workshops into a single entity to address the common challenges.

The scope of the proposed joint PDSW-DISCS workshop is summarized as:

  • Scalable storage architectures, archival storage, storage virtualization, emerging storage devices and techniques

  • Performance benchmarking, resource management, and workload studies from production systems including both traditional HPC and data-intensive workloads.

  • Programmability, APIs, and fault tolerance of storage systems

  • Parallel file systems, metadata management, and complex data management, object and key-value storage, and other emerging data storage/retrieval techniques

  • Programming models and frameworks for data intensive computing including extensions to traditional and nontraditional programming models, asynchronous multi-task programming models, or to data intensive programming models

  • Techniques for data integrity, availability and reliability especially

  • Productivity tools for data intensive computing, data mining and knowledge discovery

  • Application or optimization of emerging “big data” frameworks towards scientific computing and analysis

  • Techniques and architectures to enable cloud and container-based models for scientific computing and analysis

  • Techniques for integrating compute into a complex memory and storage hierarchy facilitating in situ and in transit data processing

  • Data filtering/compressing/reduction techniques that maintain sufficient scientific validity for large scale compute-intensive workloads

  • Tools and techniques for managing data movement among compute and data intensive components both solely within the computational infrastructure as well as incorporating the memory/storage hierarchy


CALL FOR PAPERS

 

CALL FOR PAPERS POSTER - Download to hang in your office

The Parallel Data Storage Workshop holds a peer reviewed competitive process for selecting short papers. Submit a not previously published short paper of up to 5 pages, not less than 10 point font and not including references, in a PDF file as instructed on the workshop web site. Submitted papers will be reviewed under the supervision of the workshop program committee. Submissions should indicate authors and affiliations. Final papers must not be longer than 5 pages (excluding references). Selected papers and associated talk slides will be made available on the workshop web site; the papers will also be published in the digital library of the IEEE or ACM.


SUBMISSIONS

 

Tentative Deadlines

Submissions deadline: Paper (in pdf format) due September 1, 2017
Notification: September 29, 2017
Camera ready and copyright forms due: October 10, 2017
Slides due before workshop: Sunday, November 12, 2017
* Submissions must be in the IEEE format

Paper Submission Details:

The PDSW-DISCS Workshop holds a peer reviewed competitive process for selecting short papers. Submit a not previously published short paper of up to 5 pages, not less than 10 point font and not including references, in a PDF file as instructed on the workshop web site. Submitted papers will be reviewed under the supervision of the workshop program committee. Submissions should indicate authors and affiliations. Papers must not be longer than 5 pages (excluding references). Selected papers and associated talk slides will be made available on the workshop web site; the papers will also be published in the digital libraries of the IEEE and ACM.


Work-in-progress (WIP) Submissions


wip Submissions:

There will also be a WIP session at the workshop, where presenters give 5-minute brief talks on their on-going work, with fresh problems/solutions, but may not be mature or complete yet for paper submission. A 1-page abstract is required.

WIP Submission Deadline: November 1, 2017
WIP Notification: November 7, 2017


ATTENDING THE WORKSHOP

Please be aware that all attendees to the workshop, both speakers and participants, will have to pay the SC17 registration fee. Workshops are no longer included as part of the technical program registration.

To attend the workshop, please register through the Supercomputing '17 registration page. Registration opens in July.


PROGRAM COMMITTEE:

  • Kathryn Mohror, Lawrence Livermore National Laboratory, Program Co-Chair
  • Brent Welch, Google, Program Co-Chair
  • Janine Bennett, Sandia National Laboratories
  • Angela Demke Brown, University of Toronto
  • Suren Byna, Lawrence Berkeley National Laboratory
  • Shane Canon, Lawrence Berkeley National Laboratory
  • Raghunath Raja Chandrasekar, Cray
  • Yong Chen, Texas Tech University
  • Toni Cortes, Universitat Politècnica de Catalunya
  • Garth Gibson, Carnegie Mellon
  • Elsa Gonsiorowski, Lawrence Livermore National Laboratory
  • Bingsheng He, National University of Singapore
  • Shadi Ibrahim, Inria
  • Dries Kimpe, KCG
  • Jay Lofstead, Sandia National Laboratories
  • Xiaosong Ma, Qatar Computing Research Institute
  • Carlos Maltzhan, University of California, Santa Cruz
  • Suzanne McIntosh, New York University
  • Sangmi Pallickara, Colorado State University
  • Rob Ross, Argonne National Labs
  • Philip C. Roth, Oak Ridge National Laboratory
  • Kento Sato, Lawrence Livermore National Laboratory

STEERING COMMITTEE:

  • John Bent, EMC
  • Ali R. Butt, Virginia Tech
  • Shane Canon, Lawrence Berkeley National Laboratory
  • Yong Chen, Texas Tech University
  • Evan J. Felix, Pacific Northwest National Laboratory
  • Garth A. Gibson, Carnegie Mellon University
  • William D. Gropp, University of Illinois at Urbana-Champaign
  • Gary Grider, Los Alamos National Laboratory Dean Hildebrand, IBM Research
  • Dries Kimpe, KCG, USA
  • Jay Lofstead, Sandia National Laboratories
  • Darrell Long, University of California, Santa Cruz
  • Xiaosong Ma, Qatar Computing Research Institute, Qatar
  • Carlos Maltzahn, University of California, Santa Cruz
  • Robert Ross, Argonne National Laboratory
  • Philip C. Roth, Oak Ridge National Laboratory
  • John Shalf, National Energy Research Scientific Computing Center,
    Lawrence Berkeley National Laboratory
  • Xian-He Sun, Illinois Institute of Technology
  • Rajeev Thakur, Argonne National Laboratory
  • Lee Ward, Sandia National Laboratories