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Efficient Execution of Microscopy Image Analysis on CPU, GPU, and MIC Equipped Cluster Systems.

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New scheduling techniques optimize hybrid high-performance computing systems. These methods efficiently utilize multiple processors, including graphics processing units (GPUs) and Intel Xeon Phi (MIC), for complex applications like pathology image analysis.

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Area of Science:

  • Computer Science
  • Computational Science
  • Parallel Computing

Background:

  • High-performance computing (HPC) systems are shifting towards hybrid architectures integrating central processing units (CPUs) with accelerators like graphics processing units (GPUs) and Intel Xeon Phi (MIC).
  • Despite the potential for high computational power, effectively utilizing these heterogeneous resources in real-world applications remains a significant challenge, with many applications underutilizing available devices.
  • Current applications often run on a single processor, failing to leverage the full capabilities of hybrid HPC systems.

Purpose of the Study:

  • To address the underutilization of resources in hybrid HPC systems by developing novel scheduling techniques.
  • To enable efficient execution of complex applications composed of hierarchical data flow tasks across multiple devices within a compute node.
  • To improve the performance of pathology image analysis applications on heterogeneous hardware.

Main Methods:

  • Proposed and implemented novel performance-aware scheduling techniques for allocating hierarchical data flow tasks to different devices (CPUs, GPUs, MICs) within nodes of a distributed memory machine.
  • Evaluated the proposed techniques using a pathology image analysis application focused on brain cancer morphology.
  • Compared the performance of the novel scheduling strategies against established techniques like Heterogeneous Earliest Finish Time (HEFT).

Main Results:

  • The proposed scheduling strategies significantly outperformed HEFT and other efficient techniques in cooperative executions utilizing CPUs, GPUs, and MICs.
  • Experimental results demonstrated that the novel strategies are robust to inaccuracies in scheduling input data.
  • Performance gains were sustained as the application workload scaled, indicating scalability of the proposed methods.

Conclusions:

  • The developed performance-aware scheduling techniques effectively enhance resource utilization and application performance on hybrid HPC systems.
  • These strategies offer a significant improvement over existing methods for cooperative execution on heterogeneous architectures.
  • The robustness and scalability of the proposed scheduling techniques make them suitable for a wide range of complex scientific applications.