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Monte Carlo simulation of photon migration in a cloud computing environment with MapReduce.

Guillem Pratx1, Lei Xing

  • 1Stanford University School of Medicine, Department of Radiation Oncology, 875 Blake Wilbur Drive, Stanford, California 94305, USA. pratx@stanford.edu

Journal of Biomedical Optics
|December 24, 2011
PubMed
Summary

We developed a fault-tolerant Monte Carlo simulation using MapReduce on a cloud computing environment. This approach significantly accelerates photon migration modeling, achieving a 1258x speed-up and maintaining accuracy despite hardware failures.

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Area of Science:

  • Computational physics
  • Biomedical optics
  • High-performance computing

Background:

  • Monte Carlo (MC) simulations are crucial for modeling photon migration in complex media.
  • High computational demands limit the widespread application of MC methods.
  • Need for efficient, scalable solutions in photon transport modeling.

Purpose of the Study:

  • To implement a fault-tolerant MapReduce method for accelerating Monte Carlo computations.
  • To adapt the MC321 package for parallel processing in a cloud environment.
  • To evaluate the performance and resilience of the distributed simulation.

Main Methods:

  • Porting the MC321 Monte Carlo package to the Hadoop MapReduce framework.
  • Parallel computation of photon histories by Map tasks.
  • Absorption scoring by a single Reduce task.
  • Evaluation on a commercial compute cloud.

Main Results:

  • Achieved a 1258x speed-up for simulating 100 billion photon histories using 240 nodes.
  • Demonstrated linear scaling of simulation time with photon count and inverse scaling with node count.
  • Observed a computational throughput of 85,178 photon histories/node/second.
  • Confirmed resilience to hardware failure, with 50% node shutdown not affecting simulation correctness.

Conclusions:

  • MapReduce provides a viable, fault-tolerant approach for accelerating Monte Carlo simulations in cloud environments.
  • The implemented method significantly reduces simulation time for photon migration studies.
  • This parallelization strategy enhances the accessibility and applicability of complex MC modeling.