abstract / agenda / keynote speaker / cfp / submissions / WIP session / committees
author instructions
NOTE TO PRESENTERS:
The resolution of the venue projectors is 1920x1080 / 16:9.
Bring an HDMI connector if you have one.
keynote speaker
PDSW-DISCS18 is proud to announce that Rangan Sukumar, Cray, will be our keynote speaker. Please watch for further details here.
Rangan Sukumar is an artificial intelligence researcher who architects productive and performant data solutions. He serves as the Senior Analytics Architect in the CTO’s office at Cray Inc. His role is three-fold: (i) Analytics evangelist - Demonstrating what Big Data and HPC can do for data-centric organizations, (ii) Technology visionary - Designing the roadmap for analytic products through evaluation of customer requirements and aligning them with emerging hardware and software technologies, (iii) Solutions architect - Creating bleeding-edge solutions for scientific and enterprise problems in the long-tail of the Big Data market requiring scale and performance beyond what cloud computing offers. Before his role at Cray, he served as the founding group leader, data scientist and artificial intelligence/machine learning researcher scaling algorithms on unique super-computing infrastructures at the Oak Ridge National Laboratory. He has over 70 publications in areas of disparate data collection, organization, processing, integration, fusion, analysis and inference - applied to a wide variety of domains such as healthcare, social network analysis, electric grid modernization and public policy informatics. As an entrepreneur at heart, he also serves on several technical advisory boards and committees for several startups.
agenda
| 9:00am – 9:10am |
Welcome & Introduction |
| 9:10am – 10:00am |
Keynote Speaker - Rangan Sukumar, Cray
Architectural Challenges Emerging from the Convergence of Big Data, High-Performance Computing and Artificial Intelligence
Slides |
| 10:00am – 10:30am |
Break |
| 10:30am – 11:45am |
SESSION 1
Chair: Xiaoyi Lu, Ohio State University |
| |
Integration of Burst Buffer in High-Level Parallel I/O Library for Exascale Computing Era
Kai-Yuan Hou - Northwestern University
Reda Al-Bahrani - Northwestern University
Esteban Rangel -
Northwestern University
Ankit Agrawal - Northwestern University
Robert Latham - Argonne National Laboratory
Robert Ross - Argonne National Laboratory
Alok Choudhary - Northwestern University
Wei-keng Liao - Northwestern University
Paper | Slides |
| |
Using a Robust Metadata Management System to Accelerate Scientific Discovery at Extreme Scales
Margaret Lawson - Sandia National Laboratories, University of Illinois
Jay Lofstead - Sandia National Laboratories
Paper | Slides |
| |
Evaluation of HPC Application I/O on Object Storage Systems
Jialin Liu - Lawrence Berkeley National Laboratory
Quincey Koziol - Lawrence Berkeley National Laboratory
Gregory F. Butler - Lawrence Berkeley National Laboratory
Neil Fortner - HDF Group
Mohamad Chaarawi - Intel Corporation
Houjun Tang - Lawrence Berkeley National Laboratory
Suren Byna - Lawrence Berkeley National Laboratory
Glenn K. Lockwood - Lawrence Berkeley National Laboratory
Ravi Cheema - Lawrence Berkeley National Laboratory
Kristy A. Kallback-Rose - Lawrence Berkeley National Laboratory
Damian Hazen - Lawrence Berkeley National Laboratory
Mr Prabhat - Lawrence Berkeley National Laboratory
Paper | Slides
|
| 11:45am – 12:30pm |
WIP SESSION 1
Chair: Suren Byna, Lawrence Berkeley National Laboratory |
| |
On the Challenges of Deploying an Unusual High Performance Hybrid Object/File Parallel Storage System in JASMIN
Cristina del Cano Novales, STFC, RAL
Jonathan Churchill. STFC, RAL
Athanasios Kanaris, STFC, RAL
Robert Döbbelin, Quobyte GmbH
Felix Hupfeld, Quobyte GmbH
Aleksander Trofimowicz, Quobyte GmbH
Abstract | Slides |
| |
Lazy, Minimal, Eventually Consistent IO with Stitch
Jay Lofstead, Sandia National Labs
John Mitchell, Sandia National Labs
Abstract | Slides |
| |
Scaling High-Performance Parallel File Systems in the Cloud
James Beckett, University of Washington
Eric Kim, University of Washington
Evan Stanton, University of Washington
David Liu, University of Washington
Vishank Rughwani, University of Washington
Seungmin Hwang, University of Washington
Joaquin Chung, Argonne National Laboratory
Rob Fatlandy, University of Washington
Nam Pho, University of Washington
Abstract | Slides |
| |
Towards a Task-Based I/O System
Anthony Kougkas, Illinois Institute of Technology
Hariharan Devarajan, Illinois Institute of Technology Chicago
Xian-He Sun, Illinois Institute of Technology Chicago
Abstract | Slides |
| |
Efficient Unstructured Data Compression for Block Storage Systems
Hiroki Ohtsuji, Fujitsu Laboratories Ltd.
Yoshiyasu Doi, Fujitsu Laboratories Ltd.
Abstract | Slides |
| |
Cambridge Data Accelerator
Alasdair King, University of Cambridge
Paul Calleja, University of Cambridge
Abstract | Slides |
| 12:30pm – 2:00pm |
Lunch (not provided) |
| 2:00pm – 2:50pm |
SESSION 2
Chair: Elsa Gonsiorowski, Lawrence Livermore National Laboratory |
| |
Pufferbench: Evaluating and Optimizing Malleability of Distributed Storage
Nathanael Cheriere - IRISAENS Rennes
Matthieu Dorier - Argonne National Laboratory
Gabriel Antoniu - French Institute for Research in Computer Science
and Automation (INRIA)
Paper | Slides
|
| |
Understanding SSD Reliability in Large-Scale Cloud Systems
Erci Xu - Ohio State University
Mai Zheng - Iowa State University
Feng Qin - Ohio State University
Yikang Xu - Alibaba Inc
Jiesheng Wu - Alibaba Inc
Paper | Slides
|
| 2:50pm – 3:00pm |
Reproducibility Forum
Chairs: Carlos Maltzahn, UC Santa Cruz and Ivo Jimenez, UC Santa Cruz |
| 3:00pm – 3:30pm |
Break |
| 3:30pm – 4:45pm |
SESSION 3
Chair: Jay Lofstead, Sandia National Laboratories |
| |
Characterizing Deep-Learning I/O Workloads in TensorFlow
Wei Der Chien - KTH Royal Institute of Technology
Stefano Markidis - KTH Royal Institute of Technology
Chaitanya Prasad Sishtla - KTH Royal Institute of Technology
Luis Santos - Institute Superior Técnico
Pawel Herman - KTH Royal Institute of Technology
Sai Narasimhamurthy - Seagate Systems UK
Erwin Laure - KTH Royal Institute of Technology
Paper | Slides |
| |
Toward Understanding I/O Behavior in HPC Workflows
Jakob Luttgau -
German Climate Computing Center,
Argonne National Laboratory
Shane Snyder - Argonne National Laboratory
Philip Carns - Argonne National Laboratory
Justin M. Wozniak - Argonne National Laboratory
Julian Kunkel - University of Reading
Thomas Ludwig - German Climate Computing Center
Paper | Slides |
| |
Methodology for the Rapid Development of Scalable HPC Data Services
Matthieu Dorier - Argonne National Laboratory
Philip Carns - Argonne National Laboratory
Kevin Harms - Argonne National Laboratory
Robert Latham - Argonne National Laboratory
Robert Ross - Argonne National Laboratory
Shane Snyder - Argonne National Laboratory
Justin Wozniak - Argonne National Laboratory
Samuel K. Gutiérrez - Los Alamos National Laboratory
Bob Robey - Los Alamos National Laboratory
Brad Settlemyer - Los Alamos National Laboratory
Galen Shipman - Los Alamos National Laboratory
Jerome Soumagne - HDF Group
James Kowalkowski - Fermi National Accelerator Laboratory
Marc Paterno - Fermi National Accelerator Laboratory
Saba Sehrish - Fermi National Accelerator Laboratory
Paper | Slides |
| 4:45pm – 5:30pm |
WIP SESSION 2
Chair: Jay Lofstead, Sandia National Laboratories |
| |
Parallel Algorithms for Mining Large-scale Time-varying (Dynamic) Graphs
Shaikh Arifuzzaman, University of New Orleans, New Orleans
Naw Safrin Sattar, University of New Orleans, New Orleans
Md Abdul Motaleb Faysal, University of New Orleans, New Orleans
Abstract | Slides |
| |
Initial Characterization of I/O in Large-Scale Deep Learning Applications
Fahim Chowdhury, Florida State University
Jialin Liu, Lawrence Berkeley National Laboratory
Quincey Koziol, Lawrence Berkeley National Laboratory
Thorsten Kurth, Lawrence Berkeley National Laboratory
Steven Farrell, Lawrence Berkeley National Laboratory
Suren Byna, Lawrence Berkeley National Laboratory
Prabhat, Lawrence Berkeley National Laboratory
Weikuan Yu, Florida State University
Abstract | Slides |
| |
Is End-to-end Integrity Verification Really End-to-end?
Ahmed Alhussen, University of Nevada, Reno
Batyr Charyyev, University of Nevada, Reno
Engin Arslan, University of Nevada, Reno
Abstract | Slides |
| |
Fast and Accurate Sample Transfers for Real-Time Throughput Optimization
Hemanta Sapkota, University of Nevada, Reno
Engin Arslan, University of Nevada, Reno
Abstract | Slides |
| |
Data Pallets For Traceable Data
Jay Lofstead, Sandia National Laboratories
Joshua Baker, Sandia National Laboratories
Andrew J. Younge, Sandia National Laboratories
Abstract | Slides |
| |
Asynchronous I/O Using the Earth System Modeling Framework
Matthew D. Turner, DoD HPCMP PETTT / Engility
Kevin Viner, United States Naval Research Laboratory
Tim Whitcomb, United States Naval Research Laboratory
Abstract | Slides |
WORKSHOP ABSTRACT
(Find the complete proposal outlining the merger between PDSW and DISCS here.)
We are pleased to announce that the third Joint International Workshop on Parallel Data Storage and Data Intensive Scalable Computing Systems (PDSW-DISCS’18) will be hosted at SC18: 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 prompted the integration of the PDSW and DISCS 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
This year, we are soliciting two categories of papers, regular papers and reproducibility study papers. Both will be evaluated by a competitive peer review process under the supervision of the workshop program committee. 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.
Regular paper SUBMISSIONS
We invite regular papers which may optionally include Experimental Details Appendices as described here for SC. Submissions that include experimental appendices and/or reproducibility information for automated validation (described below) will be given special consideration by the Program Committee. Authors are encouraged to include reproducibility information for automated validation of experimental results. A description of the infrastructure available for automated validation and the criteria used in validation are available here. Accepted submissions passing automated validation will earn the Results Replicated badge in the ACM DL in accordance with ACM’s artifact evaluation policy.
Deadlines
Submissions deadline: Submissions closed
Submissions website: https://submissions.supercomputing.org/
Notification: September 30, 2018
Camera ready and copyright forms due: October 5, 2018
Slides due before workshop: Friday, November 9, 2018 to jdigney@cs.cmu.edu
* Submissions must be in the IEEE conference format
NEW! REPRODUCIBILITY STUDY PAPER SUBMISSIONS
We also call for reproducibility studies that for the first time reproduce experiments from papers previously published in PDSW-DISCS or in other peer-reviewed conferences with similar topics of interest (see reproducibility study instructions). Reproducibility study submissions are selected by the same peer-reviewed competitive process as regular papers, except these submissions must pass automated validation of experimental results (see artifact evaluation criteria). Accepted submissions passing automated validation will earn the prestigious ACM “Results Replicated” Badge and, if the work under study was successfully reproduced, the associated paper will earn the ACM “Results Reproduced” Badge in the ACM DL in accordance with ACM’s artifact review and badging policy.
DEADLINES
Submissions deadline: Submissions closed
Submissions website: https://submissions.supercomputing.org/
Notification: September 30, 2018
Camera ready and copyright forms due: October 5, 2018
Slides due before workshop: Friday, November 9, 2018 to jdigney@cs.cmu.edu
* Submissions must be in the IEEE conference format.
Details on reproducibility criteria are here.
Guidelines for Regular Papers and Reproducibility Study Papers
Submit a not previously published paper as a PDF file, indicate authors and affiliations. Papers must be at least 8 pages long and no more than 12 pages long (including appendices and references). Download the IEEE conference paper template.
Details on reproducibility criteria are here.
Work-in-progress (WIP) Submissions
There will be a WIP session where presenters provide brief 5-minute talks on their on-going work, with fresh problems/solutions. WIP content is typically material that may not be mature or complete enough for a full paper submission. A one-page abstract is required.
Please email your submission to:
WIP Submission Deadline: November 1, 2018
WIP Notification: November 7, 2018
ATTENDING THE WORKSHOP
Please be aware that all
attendees to the workshop, both
speakers and participants, will have to pay the SC18 registration fee. Workshops are no longer included as part of the technical program registration.
To attend the workshop, please register through the Supercomputing '18 registration page. Registration opens July 11, 2018.
PROGRAM COMMITTEE:
- Suren Byna, Lawrence Berkeley National Laboratory
- Shane Canon, Lawrence Berkeley National Laboratory
- Yong Chen, Texas Tech University
- Toni Cortes, Universitat Politècnica de Catalunya
- Stratos Efstathiadis, NYU High Performance Computing
- Elsa Gonsiorowski, Lawrence Livermore National Laboratory
- Bingsheng He, National University of Singapore
- Jian Huang, UIUC
- Shadi Ibrahim, Inria
- Dries Kimpe, KCG
- Steve Leak, NERSC
- Jay Lofstead, Sandia National Laboratories
- Johann Lombardi, Intel
- Xiaoyi Lu, The Ohio State University
- Xiaosong Ma, Qatar Computing Research Institute
- Sangmi Pallickara, Colorado State University
- Rob Ross, Argonne National Laboratory
- Philip C. Roth, Oak Ridge National Laboratory
- Kento Sato, Lawrence Livermore National Laboratory
STEERING COMMITTEE:
- John Bent, Cray
- Ali R. Butt, Virginia Tech
- Shane Canon, Lawrence Berkeley National Laboratory
- Yong Chen, Texas Tech University
- Evan J. Felix, Pacific Northwest National Laboratory
- Gary Grider, Los Alamos National Laboratory
- William D. Gropp, University of Illinois at Urbana-Champaign
- Dean Hildebrand, Google
- Dries Kimpe, KCG, USA
- Jay Lofstead, Sandia National Laboratories
- Xiaosong Ma, Qatar Computing Research Institute, Qatar
- Carlos Maltzahn, University of California, Santa Cruz
- Kathryn Mohror, Lawrence Livermore National Laboratory
- Robert Ross, Argonne National Laboratory
- Philip C. Roth, Oak Ridge National Laboratory
- John Shalf, NERSC,
Lawrence Berkeley National Laboratory
- Xian-He Sun, Illinois Institute of Technology
- Rajeev Thakur, Argonne National Laboratory
- Lee Ward, Sandia National Laboratories
- Brent Welch, Google
