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Summary
This summary is machine-generated.

Scientists developed new software to handle massive scientific data, enabling interactive exploration and analysis on high-performance computing (HPC) systems. This infrastructure simplifies complex workflows for large-scale visualization and data processing.

Keywords:
Computing methodologies → Massively parallel algorithmsIntegrated and visual development environmentsInteractive visualization and analysisParallel programming languagesSoftware and its engineering → Development frameworks and environmentsalgorithms scalabilitymicroscopyparallel custom analysis work-flows

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Area of Science:

  • Computational science
  • Data science
  • Neuroscience

Background:

  • Modern scientific research generates massive datasets, exceeding memory and storage limits.
  • Existing software offers limited interactivity for exploring and analyzing large-scale data.
  • High-performance computing (HPC) analysis often requires complex, non-portable parallel programming.

Purpose of the Study:

  • To present a novel software infrastructure for end-to-end visualization and analysis of massive scientific data.
  • To simplify the development and deployment of analytics algorithms on HPC systems.
  • To enable interactive exploration and analysis of terabyte-scale datasets.

Main Methods:

  • A hierarchical streaming data access layer for interactive remote data exploration.
  • A library for simplified development and rapid prototyping of new analytics algorithms.
  • A scalable runtime system for automated mapping of algorithms to available computational hardware.

Main Results:

  • Demonstrated usability and performance for filtering, registration, and visualization of tera-scale microscopy data from neuroscience.
  • Evaluated system performance on the leadership-class supercomputer, Shaheen II.
  • Showcased simplified development and deployment of complex data analysis workflows.

Conclusions:

  • The developed software infrastructure effectively addresses challenges in handling massive scientific data.
  • The system enables interactive exploration, simplified algorithm development, and scalable analysis on HPC resources.
  • This approach significantly reduces the complexity of scientific data visualization and analysis workflows.