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Area of Science:

  • Computer Science
  • High-Performance Computing
  • Image Analysis

Background:

  • The Irregular Wavefront Propagation Pattern (IWPP) is crucial for image analysis but challenging to implement on co-processors like Intel Xeon Phi due to its irregular nature and reliance on atomic instructions.
  • Intel Xeon Phi co-processors require SIMD and vectorization for high performance, but lack support for SIMD atomic instructions, posing an implementation hurdle.

Purpose of the Study:

  • To propose and evaluate a novel IWPP algorithm optimized for Intel Xeon Phi co-processors, overcoming limitations of existing methods.
  • To enhance parallel algorithm efficiency by exploring the use of a priority queue as an alternative storage container for active elements.

Main Methods:

  • Developed a new IWPP algorithm designed to leverage the supported SIMD instruction set on Intel Xeon Phi.
  • Implemented and evaluated the new algorithm using Morphological Reconstruction and Imfill operations as use cases.
  • Compared the performance against the original IWPP algorithm and efficient CPU and GPU implementations.

Main Results:

  • Achieved performance improvements of up to 5.63× over the original IWPP implementation, primarily due to effective vectorization.
  • Demonstrated significant speedups of 45.7× compared to efficient CPU implementations.
  • Obtained a speedup of 1.62× when compared to efficient GPU implementations.

Conclusions:

  • The proposed IWPP algorithm effectively utilizes SIMD instructions on Intel Xeon Phi, enabling substantial performance gains in image analysis.
  • The optimized algorithm offers a compelling alternative for accelerating computationally intensive image processing tasks on modern co-processors.