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 2025, 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 system. 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 and use 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 the 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:

• Data reduction methods for scientific data

  ° Data deduplication methods

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

  ° Methods with accuracy guarantees

  ° Feature/QoI-preserving reduction

  ° Optimal design of data reduction methods

  ° Compressed sensing and singular value decomposition

• Metrics to measure reduction quality and provide feedback

• Data analysis and visualization techniques that take advantage of the reduced data

  ° AI/ML methods

  ° Surrogate/reduced-order models

  ° Feature extraction

  ° Visualization techniques

  ° Artifact removal during reconstruction

  ° Methods that take advantage of the reduced data

• Data analysis and reduction co-design

  ° Methods for using accelerators

  ° Accuracy and performance trade-offs on current and emerging hardware

  ° New programming models for managing reduced data

  ° Runtime systems for data reduction

• Large-scale code coupling and workflows

• Experience of applying data reduction and analysis in practical applications or use-cases

  ° State of the practice

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

Organizing Committee

Sheng Di, Argonne National Laboratory, USA

Ana Gainaru, Oak Ridge National Laboratory, USA

Xin Liang, University of Kentucky, USA

Kento Sato, RIKEN, Japan

Program Chair

Jieyang Chen, University of Oregon

Steering Committee

Ian Foster, Argonne National Laboratory/University of Chicago

Scott Klasky, Oak Ridge National Laboratory

Qing Liu, New Jersey Institute of Technology

Todd Munson, Argonne National Laboratory

Technical Program Committee

Tania Banerjee, University of Houston

Ayan Biswas, Los Alamos National Laboratory

Suren Byna, Ohio State University

Jon Calhoun, Clemson University

Franck Cappello, Argonne National Laboratory, University of Illinois

Jong Youl Choi, Oak Ridge National Laboratory

Sheng Di, Argonne National Laboratory, University of Chicago

Ana Gainaru, Oak Ridge National Laboratory

Qian Gong, Oak Ridge National Laboratory

Ganesh Gopalakrishnan, University of Utah

Pascal Grosset, Los Alamos National Laboratory

Xubin He, Temple University

Dan Huang, Sun Yat-sen University

Jiajun Huang, University of South Florida

Rajeev Jain, Argonne National Laboratory

Sian Jin, Temple University

Scott Klasky, Oak Ridge National Laboratory

Sidharth Kumar, University of Illinois Chicago

Guanpeng Li, University of Florida

Samuel Li, NVIDIA Corporation

Xin Liang, University of Kentucky

Peter Lindstrom, Lawrence Livermore National Laboratory

Jinyang Liu, University of Houston

Tao Lu, DapuStor Corporation

Todd Munson, Argonne National Laboratory

John Patchett, Los Alamos National Laboratory

Viktor Reshniak, Oak Ridge National Laboratory

Houjun Tang, Lawrence Berkeley National Laboratory

Robert Underwood, Argonne National Laboratory

Xiaodong Yu, Stevens Institute of Technology

Chengming Zhang, University of Houston

Kai Zhao, Florida State University