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Summary
This summary is machine-generated.

High-performance computing systems, including cloud options, accelerate Monte Carlo simulations for radiation transport modeling. Utilizing more parallel threads significantly reduces computing time for MCNP and Geant4, making cloud computing a cost-effective solution.

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Area of Science:

  • Computational physics
  • Medical physics
  • High-performance computing

Background:

  • Monte Carlo simulations are crucial for modeling radiation transport.
  • Evaluating high-performance computing (HPC) systems is essential for optimizing simulation efficiency.
  • Iodine-131 (131I) gamma-ray detection in the thyroid is a relevant application.

Purpose of the Study:

  • To compare the performance of various HPC systems for Monte Carlo simulations.
  • To identify the most efficient computing configurations for radiation transport modeling.
  • To assess the cost-effectiveness and performance of cloud computing for these simulations.

Main Methods:

  • Modeled 131I gamma-ray emission and detection using MCNP and Geant4 Monte Carlo software.
  • Benchmarked simulation performance by measuring computing time against parallel thread count.
  • Tested systems included virtual machines, desktops, a supercomputer, a science cluster, and cloud computing.

Main Results:

  • Increased parallel threads on HPC systems reduced MCNP and Geant4 simulation runtimes.
  • Cloud computing demonstrated a significant reduction in computing time.
  • Optimal cloud configurations were identified based on processors, time, and cost.

Conclusions:

  • HPC systems, particularly cloud computing, offer significant speedups for Monte Carlo radiation transport simulations.
  • Cloud computing presents a cost-effective, scalable, and on-demand solution for complex simulations.
  • Parallel processing is key to enhancing the efficiency of MCNP and Geant4 simulations.