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Application Performance Analysis and Efficient Execution on Systems with multi-core CPUs, GPUs and MICs: A Case Study

George Teodoro1, Tahsin Kurc2, Guilherme Andrade3

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Summary
This summary is machine-generated.

This study compares multi-core CPUs, GPUs, and Intel Xeon Phi (Many Integrated Core-MIC) for microscopy image analysis. Performance varied by device and data access, with new scheduling strategies improving hybrid performance.

Keywords:
Cooperative ExecutionGPGPUHybrid SystemsImage AnalysisIntel Xeon Phi

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Area of Science:

  • Computer Science
  • Computational Imaging
  • High-Performance Computing

Background:

  • Microscopy image analysis demands significant computational resources.
  • Evaluating hardware performance across different computing architectures is crucial for optimization.

Purpose of the Study:

  • To comparatively analyze the performance of multi-core CPUs, GPUs, and Intel Xeon Phi (Many Integrated Core-MIC) for microscopy image analysis.
  • To correlate performance variations with hardware characteristics and application operation attributes.
  • To develop and evaluate novel scheduling strategies for hybrid computing environments.

Main Methods:

  • Experimental performance evaluation of core operations on CPUs, GPUs, and MICs.
  • Analysis of device characteristics, data access patterns, computation complexity, and parallelization forms.
  • Development and testing of performance-aware scheduling strategies.

Main Results:

  • Observed significant performance variability across operations and computing devices.
  • MICs showed comparable or better performance than GPUs for regular data access operations.
  • GPUs outperformed MICs for irregular data access due to MIC's lower random access bandwidth.
  • Proposed scheduling strategies significantly enhanced application performance in hybrid configurations.

Conclusions:

  • Hardware choice significantly impacts microscopy image analysis performance, depending on operation characteristics.
  • Performance-aware scheduling is essential for maximizing efficiency in heterogeneous computing systems.