Multiple processor version of a Monte Carlo code for photon transport in turbid media

Colasanti, Alberto; Guida, Giovanni; Kisslinger, Annamaria; Liuzzi, Raffaele; Quarto, Maria; Riccio, Patrizia; Roberti, G.; Villani, Fulvia

doi:10.1016/s0010-4655(00)00138-7

Cited by 30 publications

(25 citation statements)

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“…Kirkby and Delpy used a 24-computer network for simulations of light transport in tissue, 26 and Colasanti et al used a CRAY parallel processor computer with 128 processors. 27 The use of multiple computers ͑or processors͒ is, however, both inconvenient and expensive, especially if such systems are to reach the extreme speed of a GPU. This is reflected by the fact that numerous areas of computational science are starting to use 20,21 programmable GPUs.…”

Section: Journal Of Biomedical Opticsmentioning

confidence: 99%

Parallel computing with graphics processing units for high-speed Monte Carlo simulation of photon migration

2008

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Section: Journal Of Biomedical Opticsmentioning

confidence: 99%

Parallel computing with graphics processing units for high-speed Monte Carlo simulation of photon migration

2008

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“…However, there existed a few parallel MC-based programs simulating photon transport [2,[12][13][14][15] in different types of materials. Versions of parallel MC programs for photon migration were previously proposed by a variety of authors [16][17][18][19][20][21]. For example, Page et al [16] developed a Java-based application with in-house-made MC-code and tested it on a distributed network of general purpose personal computers.…”

Section: Introductionmentioning

confidence: 99%

“…For example, Page et al [16] developed a Java-based application with in-house-made MC-code and tested it on a distributed network of general purpose personal computers. Colasanti et al [17] converted MC-code designed earlier by their group into a parallel version using F90 version of Fortran-language. They used ''SHared MEMory access library'' that brings communication time in distributed systems close to that of shared memory systems, using low-level features of hardware developed by Silicon Graphics Inc.…”

Section: Introductionmentioning

confidence: 99%

Parallel implementation of inverse adding-doubling and Monte Carlo multi-layered programs for high performance computing systems with shared and distributed memory

Chugunov

2015

Computer Physics Communications

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a b s t r a c tParallel implementation of two numerical tools popular in optical studies of biological materials -Inverse Adding-Doubling (IAD) program and Monte Carlo Multi-Layered (MCML) program -was developed and tested in this study. The implementation was based on Message Passing Interface (MPI) and standard Clanguage. Parallel versions of IAD and MCML programs were compared to their sequential counterparts in validation and performance tests. Additionally, the portability of the programs was tested using a local high performance computing (HPC) cluster, Penguin-On-Demand HPC cluster, and Amazon EC2 cluster. Parallel IAD was tested with up to 150 parallel cores using 1223 input datasets. It demonstrated linear scalability and the speedup was proportional to the number of parallel cores (up to 150x). Parallel MCML was tested with up to 1001 parallel cores using problem sizes of 10 4 -10 9 photon packets. It demonstrated classical performance curves featuring communication overhead and performance saturation point. Optimal performance curve was derived for parallel MCML as a function of problem size. Typical speedup achieved for parallel MCML (up to 326x) demonstrated linear increase with problem size. Precision of MCML results was estimated in a series of tests -problem size of 10 6 photon packets was found optimal for calculations of total optical response and 10 8 photon packets for spatially-resolved results. The presented parallel versions of MCML and IAD programs are portable on multiple computing platforms. The parallel programs could significantly speed up the simulation for scientists and be utilized to their full potential in computing systems that are readily available without additional costs. Program summaryProgram title: MCMLMPI, IADMPI Catalogue identifier: AEWF_v1_0Program summary URL: 65 External routines: dcmt-library (MCMLMPI), cweb-package (IADMPI) Nature of problem:Photon transport in multilayered semi-transparent material, estimation of optical properties (IADMPI) and optical response (MCMLMPI) of multilayered material samples. Solution method:Massively-parallel Monte-Carlo method (MCMLMPI), Inverse Adding-Doubling method (IADMPI) Unusual features:Tracking and analysis of photon packets in turbid media (MCMLMPI) Running time:Many small problems can be solved within seconds, large problems might take hours even on HPC clusters (MCMLMPI); hours on single computer and seconds on HPC cluster (IADMPI)

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“…7, 8, and 9) and shell scripting. 10 Light transport in turbid medium has also been ac-celerated using computer clusters, 11 multiprocessor systems, 12 and field-programmable gate arrays. 13 In recent years, the graphics processing unit has become a popular platform for running distributed biomedical computations.…”

Section: Introductionmentioning

confidence: 99%

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

Pratx

2011

J. Biomed. Opt.

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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. C 2011 Society of Photo-Optical Instrumentation Engineers (SPIE).

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Multiple processor version of a Monte Carlo code for photon transport in turbid media

Cited by 30 publications

References 1 publication

Parallel computing with graphics processing units for high-speed Monte Carlo simulation of photon migration

Parallel computing with graphics processing units for high-speed Monte Carlo simulation of photon migration

Parallel implementation of inverse adding-doubling and Monte Carlo multi-layered programs for high performance computing systems with shared and distributed memory

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

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