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 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.
Jieyang Chen, Oak Ridge National Laboratory
Ana Gainaru, Oak Ridge National Laboratory
Xin Liang, Missouri University of Science and Technology
Todd Munson, Argonne National Laboratory
Sheng Di, Argonne National Laboratory
Ian Foster, Argonne National Laboratory/University of Chicago
Scott Klasky, Oak Ridge National Laboratory
Qing Liu, New Jersey Institute of Technology
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
Papers should be submitted electronically on SC Submission Website.
Paper submission must be in IEEE format.