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Comparative Performance Analysis of Intel Xeon Phi, GPU, and CPU: A Case Study from Microscopy Image Analysis.

George Teodoro1, Tahsin Kurc2, Jun Kong3

  • 1Department of Computer Science, University of Brasília, Brasília, DF, Brazil.

IEEE Transactions on Parallel and Distributed Systems : a Publication of the IEEE Computer Society
|November 25, 2014
PubMed
Summary
This summary is machine-generated.

We evaluated operations for analyzing microscopy images on GPUs and Many Integrated Core (MIC) architectures. GPUs excel at irregular data access, while MICs perform comparably on regular data access tasks.

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Area of Science:

  • High-performance computing
  • Computational imaging
  • Scientific data analysis

Background:

  • Microscopy image analysis involves object segmentation and feature computation.
  • High-resolution sensors generate large spatial datasets requiring efficient processing.
  • Graphics Processing Units (GPUs) and Many Integrated Core (MIC) architectures offer parallel processing capabilities.

Purpose of the Study:

  • To characterize and compare the performance of key image analysis operations on GPUs and MICs.
  • To identify optimal hardware architectures for specific data access patterns in scientific applications.
  • To evaluate the effectiveness of coordinated CPU-MIC system utilization.

Main Methods:

  • Systematic implementation and performance evaluation of object segmentation and feature computation operations.
  • Testing on modern CPUs, GPUs, and MIC systems using a microscopy image analysis application.
  • Analysis of data access patterns (regular vs. irregular) and their impact on performance.
  • Examination of coordinated CPU-MIC task scheduling strategies.

Main Results:

  • MIC performance is comparable or superior to GPUs for operations with regular data access.
  • GPUs significantly outperform MICs for operations involving irregular data access due to MIC's limitations.
  • A performance-aware task scheduling strategy improved performance by 1.29× compared to first-come-first-served.
  • High efficiency (84%) achieved on a CPU-MIC system with 192 nodes.

Conclusions:

  • Hardware selection (GPU vs. MIC) should be based on the data access patterns of the application.
  • Coordinated CPU-MIC systems can achieve high performance efficiency with optimized scheduling.
  • This research provides insights for optimizing scientific image analysis on heterogeneous computing platforms.