The 7th International Workshop on Data Analysis and Reduction for Big Scientific Data (DRBSD-7)

 

In cooperation with

Held in conjunction with SC21:

The International Conference for High Performance Computing, Networking, Storage and Analysis

 

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:

• (New) AI and Data analysis over extreme-scale scientific datasets

• (New) Large-scale code coupling and workflow

 • (New) Compressed sensing

• Application use-cases which can drive the community to develop MiniApps

• Data reduction methods for scientific data including:

       o Data deduplication methods

       o Motif-specific methods (structured and unstructured meshes, particles, tensors, …)

       o Optimal design of data reduction methods

       o Methods with accuracy guarantees

• 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

 

 

Organizing Committee

Scott Klasky, Oak Ridge National Laboratory

Gary Liu, New Jersey Institute of Technology

Mark Ainsworth, Brown University

Ian Foster, Argonne National Laboratory/University of Chicago

Program Chair

 Todd Munson, Argonne National Laboratory

Technical Program Committee (tentative)

Frank Cappello, Argonne National Laboratory

Peter Lindstrom, Lawrence Livermore National Laboratory

Todd Munson, Argonne National Laboratory

John Wu, Lawrence Berkeley National Laboratory

Todd Munson, Argonne National Laboratory

Martin Burtscher, Texas State University

Dan Huang, Sun Yat-sen University

Haihang You, Institute of Computing Technology, Chinese Academy of Sciences

Xubin He, Temple University

Dingwen Tao, Washington State University

Xin Liang, Missouri University of Science and Technology

Ben Whitney, Oak Ridge National Laboratory

Sheng Di, Argonne National Laboratory

Allison Baker, NCAR

Dorit M. Hammerling, Colorado School of Mines

 

Call for Papers

The 7th International Workshop on Data Analysis and Reduction for Big Scientific Data (DRBSD-7)

Held in conjunction with SC21: The International Conference for High Performance Computing, Networking, Storage and Analysis

Nov 14th, 2021

St. Louis, MO

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.

Topics of interest include but are not limited to:

• (New) AI and Data analysis over extreme-scale scientific datasets

• (New) Large-scale code coupling and workflow

• (New) Compressed sensing

• Application use-cases which can drive the community to develop MiniApps

• Data reduction methods for scientific data including:

         o Data deduplication methods

         o Motif-specific methods (structured and unstructured meshes, particles, tensors, …)

         o Optimal design of data reduction methods

         o Methods with accuracy guarantees

• 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

Full Paper Submission Deadline: September 1st, 2021 (AoE)

Full Paper Notification: by September 15th, 2021

Extended abstracts Submission Deadline: TBD

Full Paper Notification: TBD

Submissions

Papers should be submitted electronically on SC Submission Website.

• Paper submission must be in IEEE format.

http://www.ieee.org/conferences_events/conferences/publishing/templates.html

•Papers should be submitted electronically on SC Submission Website.

* Paper submission must be in IEEE format.

http://www.ieee.org/conferences_events/conferences/publishing/templates.html

* DRBSD-7 will accept full papers (6 pages, excluding references and appendix),  and  extended abstracts (2 pages, including references and appendix).

* Submitted papers will be evaluated by at least 3 reviewers based upon technical merits.