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GPU acceleration of optical mapping algorithm for cardiac electrophysiology.

Pingfan Meng1, Ali Irturk, Ryan Kastner

  • 1Department of Computer Science and Engineering, University of California, San Diego, 9500 Gilman Dr. La Jolla, CA 92093, USA.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|February 1, 2013
PubMed
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This study presents a GPU implementation for optical mapping, significantly accelerating heart electrical activity analysis. The new method processes data 157.92X faster than optimized CPU code, improving computational efficiency.

Area of Science:

  • Biomedical Engineering
  • Computational Biology
  • Cardiovascular Physiology

Background:

  • Optical mapping is a key experimental technique for analyzing cardiac electrical activity.
  • Current optical mapping algorithms are computationally intensive, requiring significant processing time.
  • Optimized C++ programs on multi-core CPUs struggle with real-time data processing.

Purpose of the Study:

  • To develop a faster, GPU-accelerated implementation of the optical mapping algorithm.
  • To significantly reduce the computational time for analyzing cardiac optical mapping data.
  • To enhance the efficiency of processing high-resolution cardiac electrical activity videos.

Main Methods:

  • Implementation of the optical mapping algorithm on a Graphics Processing Unit (GPU).

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  • Comparison of GPU performance against a highly optimized OpenMP parallelized C++ program on a Quad-Core CPU.
  • Benchmarking processing speed in Frames Per Second (FPS) for one second of optical mapping data.

    • Main Results:

    • The GPU implementation achieved a processing speed of 578 FPS.
    • This represents a 157.92X speed improvement compared to the optimized CPU implementation.
    • The GPU approach drastically reduces the time required for optical mapping data analysis.

    Conclusions:

    • GPU acceleration offers a substantial performance enhancement for optical mapping algorithms.
    • The developed GPU implementation enables significantly faster analysis of cardiac electrical activity.
    • This advancement has the potential to accelerate research in cardiac electrophysiology and diagnostics.