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

Parallel 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 C-...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Computer physics communications Ročník 194; s. 64 - 75
Hlavní autoři: Chugunov, Svyatoslav, Li, Changying
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 01.09.2015
Témata:
Clusters
Computation
Computer simulation
computers
data collection
Inverse
Inverse Adding-Doubling
Mathematical models
memory
Monte Carlo
Monte Carlo methods
Parallel computing
Photon
Photons
physics
Simulation
Summaries
Tissue
Tissue
Parallel computing
Monte Carlo
Simulation
Photon
Inverse Adding-Doubling
ISSN:0010-4655, 1879-2944
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Parallel 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 C-language. 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 104–109 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 106 photon packets was found optimal for calculations of total optical response and 108 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 title: MCMLMPI, IADMPI Catalogue identifier: AEWF_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEWF_v1_0.html Program obtainable from: CPC Program Library, Queen’s University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 53638 No. of bytes in distributed program, including test data, etc.: 731168 Distribution format: tar.gz Programming language: C. Computer: Up to and including HPC/Cloud CPU-based clusters. Operating system: Windows, Linux, Unix, MacOS — requires ANSI C-compatible compiler. Has the code been vectorized or parallelized?: Yes, using MPI directives. RAM: From megabytes to gigabytes (MCMLMPI), kilobytes to megabytes (IADMPI) Classification: 2.2, 2.5, 18. 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)
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0010-4655
1879-2944
DOI:10.1016/j.cpc.2015.02.029