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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
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PARALLELISATION OF THE MODEL-BASED ITERATIVE RECONSTRUCTION ALGORITHM DIRA.

A Örtenberg1, M Magnusson2, M Sandborg1

  • 1Medical Radiation Physics, Department of Medical and Health Sciences and Center for Medical Image Science and Visualisation, Linköping University, Linköping SE-58185, Sweden.

Radiation Protection Dosimetry
|October 11, 2015
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Summary
This summary is machine-generated.

Parallel programming using OpenMP and OpenCL frameworks was explored for the DIRA algorithm. OpenMP achieved a 15x speedup easily, while OpenCL presented challenges but offered insights into GPU parallelization.

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Area of Science:

  • Computer Science
  • Computational Imaging
  • Software Engineering

Background:

  • Parallel programming simplifies development on multi-core processors and GPUs.
  • Parallelizing existing codebases, like the DIRA algorithm, presents significant challenges.
  • Optimizing computational tasks requires efficient parallelization strategies.

Purpose of the Study:

  • To investigate the parallelization of the DIRA algorithm using OpenMP and OpenCL.
  • To evaluate the effectiveness of these frameworks in reducing execution time.
  • To analyze the challenges and performance differences between CPU and GPU parallelization.

Main Methods:

  • Selected routines of the DIRA algorithm were parallelized using OpenMP and OpenCL.
  • MATLAB code segments were converted to C and optimized.
  • Performance was benchmarked on a 16-core computer.

Main Results:

  • OpenMP parallelization was straightforward, yielding a 15x speedup on a 16-core system.
  • OpenCL parallelization proved more complex due to CPU-GPU architectural disparities.
  • The achieved speedup with OpenCL was significantly below theoretical GPU performance, with causes identified.

Conclusions:

  • OpenMP offers an accessible and effective method for accelerating the DIRA algorithm on multi-core CPUs.
  • OpenCL presents implementation difficulties but provides valuable lessons for GPU acceleration strategies.
  • Further optimization is needed to fully leverage GPU capabilities for the DIRA algorithm.