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Title:           Parallel I/O analysis in distributed deep learning applications on high-performance computing
Subject:         The Journal of Supercomputing, https://doi.org/10.1007/s11227-025-07986-1
Keywords:        Distributed deep learning; Parallel I/O; HPC cluster; I/O behavior patterns
Author:          Edixon Parraga 
Creator:         Springer
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     Item: 
      Title: Parallel IO analysis in distributed deep learning applications on high-performance computing
      Children: 
       Item: 
        Title: Abstract
       Item: 
        Title: 1 Introduction
       Item: 
        Title: 2 Related work
       Item: 
        Title: 3 IO pattern analysis methodology for distributed deep learning parallel applications
        Children: 
         Item: 
          Title: 3.1 Characterization
          Children: 
           Item: 
            Title: 3.1.1 Application
           Item: 
            Title: 3.1.2 Dataset
           Item: 
            Title: 3.1.3 System
         Item: 
          Title: 3.2 Design of instrumentation and monitoring
          Children: 
           Item: 
            Title: 3.2.1 Application
           Item: 
            Title: 3.2.2 Dataset
           Item: 
            Title: 3.2.3 System
         Item: 
          Title: 3.3 IO tracing and instrumentation
         Item: 
          Title: 3.4 Trace analysis
         Item: 
          Title: 3.5 Behavior Description
          Children: 
           Item: 
            Title: 3.5.1 Illustrative Case: Behavior Description with the DLIO Benchmark for HDF5, NPZ, and TFRecord File Formats
         Item: 
          Title: 3.6 Performance behavior
          Children: 
           Item: 
            Title: 3.6.1 Illustrative case: Performance behavior with DLIO benchmark across file formats (HDF5, NPZ, TFRecord)
         Item: 
          Title: 3.7 Configuration guidelines
       Item: 
        Title: 4 Case study: DeepGalaxy application
        Children: 
         Item: 
          Title: 4.1 Characterization
          Children: 
           Item: 
            Title: 4.1.1 Application characterization
           Item: 
            Title: 4.1.2 Datasets characterization
           Item: 
            Title: 4.1.3 System characterization
         Item: 
          Title: 4.2 Design of instrumentation and monitoring
          Children: 
           Item: 
            Title: 4.2.1 DL application
           Item: 
            Title: 4.2.2 Dataset
           Item: 
            Title: 4.2.3 System
         Item: 
          Title: 4.3 IO tracing and instrumentation
         Item: 
          Title: 4.4 Trace analysis
         Item: 
          Title: 4.5 Behavior description
         Item: 
          Title: 4.6 Analysis of the spatial and temporal behavior of IO
          Children: 
           Item: 
            Title: 4.6.1 Performance analysis of the DeepGalaxy application on Lustre file system
         Item: 
          Title: 4.7 Comparative analysis of IO patterns: DLIO vs. DeepGalaxy
       Item: 
        Title: 5 Comparative analysis of Lustre file system configurations with different OST counts
        Children: 
         Item: 
          Title: 5.1 Performance metrics for the DeepGalaxy application on Lustre with different OST settings (Data loading mode: Shared)
         Item: 
          Title: 5.2 Performance metrics for the DeepGalaxy application on Lustre with different OST settings (Data loading mode: Shared (reload+shuffle)
         Item: 
          Title: 5.3 Comparative conclusion between Shared and Shared (reload+shuffle) modes
       Item: 
        Title: 6 Configuration guidelines
        Children: 
         Item: 
          Title: 6.1 Interpretation and use of the configuration guidance table
       Item: 
        Title: 7 Conclusions
       Item: 
        Title: Acknowledgements 
       Item: 
        Title: References
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    Title: Parallel I/O analysis in distributed deep learning applications on high-performance computing
    Author: Edixon Parraga 
    Subject: The Journal of Supercomputing, https://doi.org/10.1007/s11227-025-07986-1
    Keywords: Distributed deep learning; Parallel I/O; HPC cluster; I/O behavior patterns
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