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Updated: May 1, 2026

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ComPRePS 2.0: Enabling Massive-Scale Distributed Computing on High-Performance Computing Cluster for

Suhas Katari Chaluva Kumar1,2, Anindya S Paul1, Haitham Abdelazim1

  • 1Dept. of Medicine - Section of Quantitative Health, University of Florida, Gainesville, FL.

Proceedings of Spie--The International Society for Optical Engineering
|March 9, 2026
PubMed
Summary
This summary is machine-generated.

ComPRePS 2.0 enhances computational pathology by integrating high-performance computing for large-scale analysis of whole-slide images (WSI). This AI-driven tool significantly improves scalability and security for kidney disease research.

Keywords:
AI/ML Image AnalysisDistributed computingGPU-accelerated computeHigh performance computing (HPC)HiperGatorHistopathology AnalysisScalable AI systems

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Area of Science:

  • Computational pathology
  • Digital pathology
  • Health informatics

Background:

  • The digitization of histological data into whole-slide images (WSI) has driven advancements in computational pathology.
  • Large-scale, high-resolution image processing is essential for analyzing complex biomedical data, especially private patient information.
  • Existing computational pathology tools face limitations in scalability and security for massive datasets.

Purpose of the Study:

  • To develop an advanced computational pathology suite (ComPRePS 2.0) addressing the scalability and security limitations of its predecessor.
  • To leverage high-performance computing clusters (HPCCs) for efficient processing of gigapixel WSI.
  • To enhance AI-driven analysis of histopathological slides for clinical tasks and research.

Main Methods:

  • Integration of the Computational Renal Pathology Suite (ComPRePS 2.0) with the University of Florida's HiperGator HPCC.
  • Utilization of on-demand CPU, GPU, and memory resources.
  • Implementation of Apptainer-based containerization and parallel file system access for distributed computing.

Main Results:

  • ComPRePS 2.0 demonstrated a 15x performance improvement compared to ComPRePS 1.0.
  • Achieved unprecedented scalability and enhanced security for processing large-dimensional WSIs.
  • Successfully processed 920 large-dimensional WSIs, generating crucial data for kidney disease research.

Conclusions:

  • ComPRePS 2.0 provides a scalable and secure platform for automated histopathological slide analysis.
  • The integration with HPCCs significantly boosts computational power for digital pathology workloads.
  • This advancement facilitates large-scale data analysis, benefiting researchers, pathologists, and students in understanding kidney disease progression.