TU‐FF‐A1‐06: A Robust Scalable Parallel Processing System for Radiation Therapy

S. Morrill, Brent Parker, C. Brack

Research output: Contribution to journalArticle

Abstract

Purpose: To develop a robust Linux—based cluster for the parallel computation of problems of interest in a radiation therapy environment. This system should be robust, scalable and easy to manage. It should be constructed from commercially available low cost hardware and use only open source software tools to manage the system. Method and Materials: This cluster was constructed using a distributed memory model with the Message Passing Interface (MPI) protocol. The use of distributed memory requires a fast backbone to efficiently distribute programs and data to the various cluster nodes. This rapid data transfer was accomplished using a Gigabit Ethernet switch which allows a peak transfer rate of 100 Mbytes/sec. The cluster currently consists of 76 CPU's each with a minimum of 512 Mbytes of RAM. The individual nodes run an open source version (Centos 4.0) of the Redhat Enterprise 4.0 Linux operating system. The MPI protocol is implemented using the open source implementation, MPICH. Cluster node management is accomplished using the ROCKS 4.0 shareware toolset. The compilers and debuggers (C++ and FORTRAN for Linux) are Intel 9.0. Finally, the Integrated Development Environment (IDE) is the Eclipse open‐source project v3.0.1 with PHOTRAN extensions. Results: This cluster has recently been commissioned and several benchmark tests have been completed. Factors of 15X – 60X improvement in speed for parallelizable sections of various codes have been demonstrated. Conclusions: This system is robust enough to solve complex problems which were intractable with our previous computational tools. The demonstrated speed improvements will allow for the implementation of codes for problems such as: real‐time dose calculation, fast IMRT optimization, and the convolution of correlated CT datasets to account for patient motion.

Original languageEnglish (US)
Number of pages1
JournalMedical Physics
Volume33
Issue number6
DOIs
StatePublished - 2006

Fingerprint

Benchmarking
Radiotherapy
Software
Costs and Cost Analysis
Datasets

ASJC Scopus subject areas

  • Biophysics
  • Radiology Nuclear Medicine and imaging

Cite this

TU‐FF‐A1‐06 : A Robust Scalable Parallel Processing System for Radiation Therapy. / Morrill, S.; Parker, Brent; Brack, C.

In: Medical Physics, Vol. 33, No. 6, 2006.

Research output: Contribution to journalArticle

@article{58a662cffcb14ebba4d3d6fb5a2f6126,
title = "TU‐FF‐A1‐06: A Robust Scalable Parallel Processing System for Radiation Therapy",
abstract = "Purpose: To develop a robust Linux—based cluster for the parallel computation of problems of interest in a radiation therapy environment. This system should be robust, scalable and easy to manage. It should be constructed from commercially available low cost hardware and use only open source software tools to manage the system. Method and Materials: This cluster was constructed using a distributed memory model with the Message Passing Interface (MPI) protocol. The use of distributed memory requires a fast backbone to efficiently distribute programs and data to the various cluster nodes. This rapid data transfer was accomplished using a Gigabit Ethernet switch which allows a peak transfer rate of 100 Mbytes/sec. The cluster currently consists of 76 CPU's each with a minimum of 512 Mbytes of RAM. The individual nodes run an open source version (Centos 4.0) of the Redhat Enterprise 4.0 Linux operating system. The MPI protocol is implemented using the open source implementation, MPICH. Cluster node management is accomplished using the ROCKS 4.0 shareware toolset. The compilers and debuggers (C++ and FORTRAN for Linux) are Intel 9.0. Finally, the Integrated Development Environment (IDE) is the Eclipse open‐source project v3.0.1 with PHOTRAN extensions. Results: This cluster has recently been commissioned and several benchmark tests have been completed. Factors of 15X – 60X improvement in speed for parallelizable sections of various codes have been demonstrated. Conclusions: This system is robust enough to solve complex problems which were intractable with our previous computational tools. The demonstrated speed improvements will allow for the implementation of codes for problems such as: real‐time dose calculation, fast IMRT optimization, and the convolution of correlated CT datasets to account for patient motion.",
author = "S. Morrill and Brent Parker and C. Brack",
year = "2006",
doi = "10.1118/1.2241643",
language = "English (US)",
volume = "33",
journal = "Medical Physics",
issn = "0094-2405",
publisher = "AAPM - American Association of Physicists in Medicine",
number = "6",

}

TY - JOUR

T1 - TU‐FF‐A1‐06

T2 - A Robust Scalable Parallel Processing System for Radiation Therapy

AU - Morrill, S.

AU - Parker, Brent

AU - Brack, C.

PY - 2006

Y1 - 2006

N2 - Purpose: To develop a robust Linux—based cluster for the parallel computation of problems of interest in a radiation therapy environment. This system should be robust, scalable and easy to manage. It should be constructed from commercially available low cost hardware and use only open source software tools to manage the system. Method and Materials: This cluster was constructed using a distributed memory model with the Message Passing Interface (MPI) protocol. The use of distributed memory requires a fast backbone to efficiently distribute programs and data to the various cluster nodes. This rapid data transfer was accomplished using a Gigabit Ethernet switch which allows a peak transfer rate of 100 Mbytes/sec. The cluster currently consists of 76 CPU's each with a minimum of 512 Mbytes of RAM. The individual nodes run an open source version (Centos 4.0) of the Redhat Enterprise 4.0 Linux operating system. The MPI protocol is implemented using the open source implementation, MPICH. Cluster node management is accomplished using the ROCKS 4.0 shareware toolset. The compilers and debuggers (C++ and FORTRAN for Linux) are Intel 9.0. Finally, the Integrated Development Environment (IDE) is the Eclipse open‐source project v3.0.1 with PHOTRAN extensions. Results: This cluster has recently been commissioned and several benchmark tests have been completed. Factors of 15X – 60X improvement in speed for parallelizable sections of various codes have been demonstrated. Conclusions: This system is robust enough to solve complex problems which were intractable with our previous computational tools. The demonstrated speed improvements will allow for the implementation of codes for problems such as: real‐time dose calculation, fast IMRT optimization, and the convolution of correlated CT datasets to account for patient motion.

AB - Purpose: To develop a robust Linux—based cluster for the parallel computation of problems of interest in a radiation therapy environment. This system should be robust, scalable and easy to manage. It should be constructed from commercially available low cost hardware and use only open source software tools to manage the system. Method and Materials: This cluster was constructed using a distributed memory model with the Message Passing Interface (MPI) protocol. The use of distributed memory requires a fast backbone to efficiently distribute programs and data to the various cluster nodes. This rapid data transfer was accomplished using a Gigabit Ethernet switch which allows a peak transfer rate of 100 Mbytes/sec. The cluster currently consists of 76 CPU's each with a minimum of 512 Mbytes of RAM. The individual nodes run an open source version (Centos 4.0) of the Redhat Enterprise 4.0 Linux operating system. The MPI protocol is implemented using the open source implementation, MPICH. Cluster node management is accomplished using the ROCKS 4.0 shareware toolset. The compilers and debuggers (C++ and FORTRAN for Linux) are Intel 9.0. Finally, the Integrated Development Environment (IDE) is the Eclipse open‐source project v3.0.1 with PHOTRAN extensions. Results: This cluster has recently been commissioned and several benchmark tests have been completed. Factors of 15X – 60X improvement in speed for parallelizable sections of various codes have been demonstrated. Conclusions: This system is robust enough to solve complex problems which were intractable with our previous computational tools. The demonstrated speed improvements will allow for the implementation of codes for problems such as: real‐time dose calculation, fast IMRT optimization, and the convolution of correlated CT datasets to account for patient motion.

UR - http://www.scopus.com/inward/record.url?scp=85024782197&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85024782197&partnerID=8YFLogxK

U2 - 10.1118/1.2241643

DO - 10.1118/1.2241643

M3 - Article

AN - SCOPUS:85024782197

VL - 33

JO - Medical Physics

JF - Medical Physics

SN - 0094-2405

IS - 6

ER -