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Optimizing High-Performance Computing Systems for Biomedical Workloads.

Patricia Kovatch1, Lili Gai2, Hyung Min Cho2

  • 1Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, USA.

IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum : [Proceedings]. IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum
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Summary
This summary is machine-generated.

Optimizing high-performance computing (HPC) systems for computational biology workflows enhances scientific discovery. Strategic system tuning and upgrades significantly increase research throughput and publication output.

Keywords:
cloud technologiescomputational biologygenomicshigh performance computingparallel file systemsschedulingsustainabilitysystem optimization

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Area of Science:

  • Computational Biology
  • High-Performance Computing (HPC)
  • Bioinformatics

Background:

  • Computational biologist productivity is often limited by slow workflows and non-optimized computing systems.
  • Biomedical researchers prioritize scientific discovery over code optimization, hindering progress.
  • Many life science applications underutilize HPC capabilities, necessitating system tuning.

Purpose of the Study:

  • To present a case study on optimizing HPC systems for biomedical computational workloads.
  • To demonstrate strategies for enhancing scientific throughput and data fidelity.
  • To share practical approaches for sustainable HPC system growth and management.

Main Methods:

  • Analyzed the impact of computational applications on system components (cores, file system, resource manager).
  • Iteratively upgraded HPC system architecture, including scheduling, memory, compute, and file system capabilities.
  • Implemented a chargeback fee structure for long-term system stability and sustainability.

Main Results:

  • Evolved a 70 teraflop system to a 1.4 petaflop system over seven years, supporting a nearly 10-fold user base growth.
  • Enabled over 900 biomedical publications across genetics, gene expression, machine learning, and structural/chemical biology.
  • Maintained minimal impact on existing user workflows and code during system optimization.

Conclusions:

  • Effective HPC system stewardship requires understanding application-system interactions for optimization.
  • Strategic system upgrades and sustainable management plans are crucial for enabling long-term computational and data science productivity.
  • Continuous assessment and adaptation are key to addressing the unique challenges of optimizing HPC for scientific research.