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DAX - The Next Generation: Towards One Million Processes on Commodity Hardware.

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We optimized the Distributed Automation for XNAT (DAX) platform for large-scale neuroimaging projects. New methods significantly reduce processing times and database load, enabling efficient handling of massive datasets.

Keywords:
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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Big Data Analytics

Background:

  • Large-scale neuroimaging projects require efficient storage, job distribution, and scheduling.
  • The eXtensible Neuroimaging Archive Toolkit (XNAT) addresses storage but faces bottlenecks with massive datasets (>100,000 assessors).
  • Existing Distributed Automation for XNAT (DAX) system shows performance limitations.

Purpose of the Study:

  • To optimize the DAX platform for large-scale neuroimaging data processing.
  • To address bottlenecks in XNAT assessor generation and job management for projects exceeding 100,000 data points.
  • To enhance the efficiency and scalability of neuroimaging data pipelines.

Main Methods:

  • Developed a new API for direct database connection to expedite assessor generation, bypassing REST API calls.
  • Implemented DISKQ for on-disk tracking of processing status, reducing XNAT API calls.
  • Integrated DAX into Docker containers for streamlined deployment and execution of image processing pipelines.

Main Results:

  • Reduced time to create 1,000 assessors by over 270-fold (65,040s to 229s).
  • Optimized DISKQ with pyXnat enabled launching 400 jobs in under 10 seconds (previously 2,000s).
  • Significantly decreased XNAT load by reducing API calls through disk-based status tracking.

Conclusions:

  • The enhanced DAX platform, with its new API and DISKQ optimization, is capable of supporting neuroimaging projects with hundreds of thousands of scans.
  • The implemented optimizations dramatically improve processing time-efficiency for large-scale data.
  • These advancements position DAX as a robust solution for managing and processing massive neuroimaging datasets.