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:

  ° Data deduplication methods

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

  ° Optimal design of data reduction methods

  ° 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


• Papers should be submitted electronically on SC Submission Website.

• Paper submission must be in IEEE format.

• 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.