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GATE Monte Carlo simulation of dose distribution using MapReduce in a cloud computing environment.

Yangchuan Liu1,2, Yuguo Tang1, Xin Gao3

  • 1Medical Imaging Department, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, No. 88 Keling Rd, Suzhou, 215163, Jiangsu, China.

Australasian Physical & Engineering Sciences in Medicine
|September 2, 2017
PubMed
Summary

This study introduces a cloud computing method using MapReduce for faster GATE Monte Carlo simulations. The novel approach accurately calculates dose distribution, significantly reducing simulation time for medical physics applications.

Keywords:
Cloud computingGATEHadoopMapReduceMonte Carlo

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Area of Science:

  • Medical Physics
  • Computational Science

Background:

  • Accurate dose calculation is crucial for radiation therapy planning and quality assurance.
  • The Geant4 Application for Tomographic Emission (GATE) Monte Carlo platform is widely used but computationally intensive.
  • Existing GATE simulations for dose distribution are time-consuming, limiting their practical application.

Purpose of the Study:

  • To implement and evaluate a novel cloud computing method for accelerating GATE Monte Carlo simulations.
  • To leverage MapReduce and Hadoop on Amazon Elastic Compute Cloud (EC2) for parallel dose distribution calculations.
  • To assess the accuracy, scalability, and fault tolerance of the proposed cloud-based GATE simulation approach.

Main Methods:

  • Developed an Amazon Machine Image with Hadoop and GATE for cloud-based cluster setup.
  • Implemented a MapReduce framework where GATE input files (macros) were split and processed in parallel.
  • Utilized Hadoop Streaming to execute GATE simulations on Map tasks and aggregate results in Reduce tasks.
  • Evaluated the method using GATE simulations in a water phantom for 6 and 18 MeV X-ray photons.

Main Results:

  • The cloud-based parallel simulation achieved accuracy comparable to single-threaded local simulations.
  • Simulation correctness was maintained even with the failure of worker nodes, demonstrating fault tolerance.
  • Simulation time was inversely proportional to the number of worker nodes.
  • A 10 million photon simulation on 64 nodes showed 41x and 32x speedups compared to single-worker and single-threaded cases, respectively.

Conclusions:

  • The proposed MapReduce-based cloud computing method offers a feasible and efficient solution for accelerating GATE Monte Carlo simulations.
  • This approach significantly reduces computation time for dose distribution calculations without compromising accuracy.
  • The method enhances the practicality of GATE for treatment planning and quality assurance in medical physics.