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Managing a Heterogeneous Cluster.

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Managing a complex, heterogeneous high-performance computing (HPC) cluster requires a strategic approach. This paper details a successful strategy for integrating diverse hardware and software, enabling scalability and efficient resource utilization for advanced research computing.

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Area of Science:

  • High-performance computing (HPC)
  • Computer systems engineering
  • Computational science

Background:

  • Traditional HPC clusters often use homogeneous hardware, replaced periodically.
  • This study focuses on a cluster with continuously integrated, diverse hardware over time.
  • The cluster comprises over 300 compute nodes, 8000+ cores, 7 network technologies, 102 GPUs, and 28 storage nodes from multiple vendors and generations.

Purpose of the Study:

  • To present a management strategy for a highly heterogeneous and complex HPC system.
  • To demonstrate how to overcome management challenges in diverse computing environments.
  • To provide a model for other heterogeneous systems and smaller clusters considering expansion.

Main Methods:

  • Implementing a unified strategy for software optimization and operating system consistency.
  • Establishing robust identity management and resource prioritization frameworks.
  • Integrating diverse network technologies (Ethernet, Infiniband, OmniPath) and storage solutions.
  • Utilizing a combination of open-source and in-house developed software for cluster management.

Main Results:

  • Successful integration and management of a large-scale, heterogeneous HPC cluster.
  • Demonstrated advantages of system diversity despite increased management complexity.
  • Development of a scalable management model applicable to various cluster sizes.

Conclusions:

  • A strategic approach combining open-source and custom software effectively manages heterogeneous HPC environments.
  • This management strategy facilitates the integration of diverse hardware, enhancing research capabilities.
  • The presented model can alleviate management concerns for smaller clusters aiming for expansion.