Abstract
A growing disparity between simulation speeds and I/O rates makes it increasingly infeasible for high-performance applications to save all results for offline analysis. By 2024, computers are expected to compute at 1018 ops/sec but write to disk only at 1012 bytes/sec: a compute-to-output ratio 200 times worse than on the first petascale systems. In this new world, applications must increasingly perform online data analysis and reduction—tasks that introduce algorithmic, implementation, and programming model challenges that are unfamiliar to many scientists and that have major implications for the design of various elements of exascale systems.
This trend has spurred interest in high-performance online data analysis and reduction methods, motivated by a desire to conserve I/O bandwidth, storage, and/or power; increase accuracy of data analysis results; and/or make optimal use of parallel platforms, among other factors. This requires our community to understand a clear yet complex relationships between application design, data analysis and reduction methods, programming models, system software, hardware, and other elements of a next-generation High Performance Computer, particularly given constraints such as applicability, fidelity, performance portability, and power efficiency.
There are at least three important topics that our community is striving to answer: (1) whether several orders of magnitude of data reduction is possible for exascale sciences; (2) understanding the performance and accuracy trade-off of data reduction; and (3) solutions to effectively reduce data while preserving the information hidden in large scientific data. Tackling these challenges requires expertise from computer science, mathematics, and application domains to study the problem holistically, and develop solutions and hardened software tools that can be used by production applications.
The goal of this workshop is to provide a focused venue for researchers in all aspects of data reduction and analysis to present their research results, exchange ideas, identify new research directions, and foster new collaborations within the community.
Topics of interest include but are not limited to:
• Application use-cases which can drive the community to develop MiniApps
• Data reduction methods for scientific data including:
• Metrics to measure reduction quality and provide feedback
• Data analysis and visualization techniques that take advantage of the reduced data
• Hardware and data co-design
• Accuracy and performance trade-offs on current and emerging hardware
• New programming models for managing reduced data
• Runtime systems for data reduction
Agenda
9:00 – 9:15 Opening Remarks: Dr. Scott Klasky
9:15 – 10:00 Keynote: Dr. Jack Wells – TBD
10:00 – 10:30 Axel Huebl, On the Scalability of Data Reduction Techniques in Current and Upcoming HPC Systems from an Application Perspective
10:30 : 11:00 Allison Baker, Toward a Multi-method Approach: Lossy Data Compression for Climate Simulation Data
11:00 – 11:30 BREAK
11:30 – 12:00 Invited talk: Prof. Ian Foster, CODAR
12:00 – 12:30 Dingwhen Tao, Exploration of Pattern-Matching Techniques for Lossy Compression on Cosmology Simulation Data Sets
12:30 – 1:00 Anastasiia Novikova, Decoupling the Selection of Compression Algorithms from Quality Constraints with SCIL
Organizing Committee
Scott Klasky, Oak Ridge National Laboratory
Gary Liu, New Jersey Institute of Technology
Mark Ainsworth, Brown University/Oak Ridge National Laboratory
Ian Foster, Argonne National Laboratory/University of Chicago
Technical Program Committee
Frank Cappello, Argonne National Laboratory
Peter Lindstrom, Lawrence Livermore National Laboratory
Tamara Kolda, Sandia National Laboratory
Todd Munson, Argonne National Laboratory
George Ostrouchov, Oak Ridge National Laboratory
Scott Klasky, Oak Ridge National Laboratory
Mark Ainsworth, Brown University/Oak Ridge National Laboratory
John Wu, Lawrence Berkeley National Laboratory
Todd Munson, Argonne National Laboratory
Eric Suchyta, Oak Ridge National Laboratory
Martin Burtscher, Texas State University
Call for Papers
The 1st International Workshop on Data Reduction for Big Scientific Data (DRBSD-1)
in Conjunction with ISC’17
June 22nd, 2017
Frankfurt, Germany
As the speed gap between compute and storage continues to exist and widen, the increasing data volume and velocity pose major challenges for big data applications in terms of storage and analysis. This demands new research and software tools that can further reduce data by several orders of magnitude, taking advantage of new architectures and hardware available on next generation systems. This international workshop on data reduction is a response to this renewed research direction and will provide a focused venue for researchers in this area to present their research results, exchange ideas, identify new research directions, and foster new collaborations within the community.
Topics of interest include but are not limited to:
• Application use-cases which can drive the community to develop MiniApps
• Data reduction methods for scientific data including:
• Metrics to measure reduction quality and provide feedback
• Data analysis and visualization techniques that take advantage of the reduced data
• Hardware and data co-design
• Accuracy and performance trade-offs on current and emerging hardware
• New programming models for managing reduced data
• Runtime systems for data reduction
Important Dates
Paper Deadline: extended to April 15th, 2017 (AoE)
Author Notification: April 30th, 2017
Camera Ready Final Papers: June 15th, 2017 (AoE)
Submissions
Papers should be submitted electronically on Easychair (https://easychair.org/conferences/?conf=drbsd1).
• General information on paper format can be found on Springers LNCS “Information for Authors of Computer Science Publications”.
• Paper submissions are required to be within 10 pages excluding references.
• Submitted papers will be evaluated by at least 3 reviewers based upon technical merits and the accepted paper will be published with Springer.