Evidence map›Paper›PMID 21841211›Full record

ArticlePhysics in medicine and biology2011

Toward real-time Monte Carlo simulation using a commercial cloud computing infrastructure.

Henry Wang, Yunzhi Ma, Guillem Pratx, Lei Xing

Abstract readEvaluation Study
In one paragraph

Article in Physics in medicine and biology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Article
  6. Monte Carlo verification of radiotherapy treatments with CloudMC.Radiation oncology (London, England) · 2018
    Article
  7. Article
  8. Article
  9. Article
  10. Internet-based computer technology on radiotherapy.Reports of practical oncology and radiotherapy : journal of Greatpoland Cancer Center in Poznan and Polish Society of Radiation Oncology
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Henry WangDepartment of Electrical Engineering, Stanford University, Stanford, CA 94305, USA. hwang41@stanford.edu
Yunzhi Ma
Guillem Pratx
Lei Xing

Funding

Computational Tools for Adaptive Radiation TherapyR01CA133474 · NCI · STANFORD UNIVERSITY · PI XING, LEI · 2009 to 2012
$960k
Clinical Evaluation of Real-Time MV/kV Image Guided Prostate Radiation TherapyR21CA153587 · NCI · STANFORD UNIVERSITY · PI XING, LEI · 2010 to 2011
$654k
NCI NIH HHS 1R01 CA133474NCI NIH HHS 1R21 CA153587NCI NIH HHS R01 CA133474NCI NIH HHS R21 CA153587
6 · The paper itself

Abstract

Monte Carlo (MC) methods are the gold standard for modeling photon and electron transport in a heterogeneous medium; however, their computational cost prohibits their routine use in the clinic. Cloud computing, wherein computing resources are allocated on-demand from a third party, is a new approach for high performance computing and is implemented to perform ultra-fast MC calculation in radiation therapy. We deployed the EGS5 MC package in a commercial cloud environment. Launched from a single local computer with Internet access, a Python script allocates a remote virtual cluster. A handshaking protocol designates master and worker nodes. The EGS5 binaries and the simulation data are initially loaded onto the master node. The simulation is then distributed among independent worker nodes via the message passing interface, and the results aggregated on the local computer for display and data analysis. The described approach is evaluated for pencil beams and broad beams of high-energy electrons and photons. The output of cloud-based MC simulation is identical to that produced by single-threaded implementation. For 1 million electrons, a simulation that takes 2.58 h on a local computer can be executed in 3.3 min on the cloud with 100 nodes, a 47× speed-up. Simulation time scales inversely with the number of parallel nodes. The parallelization overhead is also negligible for large simulations. Cloud computing represents one of the most important recent advances in supercomputing technology and provides a promising platform for substantially improved MC simulation. In addition to the significant speed up, cloud computing builds a layer of abstraction for high performance parallel computing, which may change the way dose calculations are performed and radiation treatment plans are completed.

Indexed as

Computer SimulationInternetMonte Carlo MethodSoftwareAlgorithmsCluster AnalysisComputing MethodologiesHumansInformation Storage and RetrievalPhantoms, ImagingRadiotherapyRadiotherapy Planning, Computer-Assisted

Identifiers

PMID21841211
PMCPMC3431188

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.