[hpc-announce] The 5th International Workshop on Data Analysis and Reduction for Big Scientific Data (DRBSD-5) in Conjunction with SC'19

Liu, Qing qing.liu at njit.edu
Wed May 1 11:14:22 CDT 2019


This year's workshop has significantly expanded the scope, and
additionally solicits submissions on large-scale scientific data
analysis, including AI, machine learning, in addition to data
reduction.  Below please find the call for paper.

Call for Papers

The 5th International Workshop on Data Analysis and Reduction for Big
Scientific Data (DRBSD-5)


in Conjunction with SC’19

Nov 17th, 2019

Denver, CO

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

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

* Data reduction methods for scientific data including:

         1. Data deduplication methods

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

         3. Optimal design of data reduction methods

         4. 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

Paper Deadline: September 20th, 2019 (AoE)

Author Notification: by September 30th, 2019


Papers should be submitted electronically on SC Submission Website.

* Paper submission must be in IEEE format.


* Paper submissions are required to be 6 pages excluding references.

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

Dr. Qing Liu
Assistant Professor
Helen and John C. Hartmann
Department of Electrical and Computer Engineering
New Jersey Institute of Technology, Newark, NJ
Web: https://web.njit.edu/~qliu/
Email: qing.liu at njit.edu
Phone: 973-596-3526

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