ArticleJournal of biomedical optics2011
Monte Carlo simulation of photon migration in a cloud computing environment with MapReduce.
Article in Journal of biomedical optics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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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.
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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.
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Who cites it
6 citing papers in PubMed.
- Closed Home Physical Education Teaching Model and Response Strategies Based on Big Data Technology.Journal of environmental and public health · 2022Article
- A Novel GPU-based Fast Monte Carlo Photon Dose Calculating Method for Accurate Radiotherapy Treatment Planning.Journal of biomedical physics & engineering · 2020Article
- Independent calculation of monitor units for VMAT and SPORT.Medical physics · 2015Article
- GPU-based high-performance computing for radiation therapy.Physics in medicine and biology · 2014Review
- Toward a web-based real-time radiation treatment planning system in a cloud computing environment.Physics in medicine and biology · 2013Article
- Ultrafast and scalable cone-beam CT reconstruction using MapReduce in a cloud computing environment.Medical physics · 2011Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Monte Carlo simulation is considered the most reliable method for modeling photon migration in heterogeneous media. However, its widespread use is hindered by the high computational cost. The purpose of this work is to report on our implementation of a simple MapReduce method for performing fault-tolerant Monte Carlo computations in a massively-parallel cloud computing environment. We ported the MC321 Monte Carlo package to Hadoop, an open-source MapReduce framework. In this implementation, Map tasks compute photon histories in parallel while a Reduce task scores photon absorption. The distributed implementation was evaluated on a commercial compute cloud. The simulation time was found to be linearly dependent on the number of photons and inversely proportional to the number of nodes. For a cluster size of 240 nodes, the simulation of 100 billion photon histories took 22 min, a 1258 × speed-up compared to the single-threaded Monte Carlo program. The overall computational throughput was 85,178 photon histories per node per second, with a latency of 100 s. The distributed simulation produced the same output as the original implementation and was resilient to hardware failure: the correctness of the simulation was unaffected by the shutdown of 50% of the nodes.
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Registered trials
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.