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SU‐E‐E‐05: The Compute Cloud, a Massive Computing Resource for Patient‐Independent Monte Carlo Dose Calculations and Other Medical Physics Applications
Author(s) -
Constantin M,
Sawkey D,
Mansfield S,
Svatos M
Publication year - 2011
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.3611559
Subject(s) - computer science , cloud computing , monte carlo method , virtual machine , upload , workstation , supercomputer , computational science , operating system , mathematics , statistics
Purpose: Massive computing resources are needed to generate an extensive linac phase‐space (PHSP) database for different radiotherapy beams that could serve as a starting point for several user‐applications, such as patient‐dependent dose calculations or Monte‐Carlo‐based quality assurance procedures. The goal of this project is to disseminate our knowledge about performing patient‐independent Monte‐Carlo dose calculations on the Amazon Elastic Compute Cloud (EC2) and educate medical physicists on technical aspects such as simulation instance management and cluster coordination while ensuring data security for a low CPU cost. Methods: Amazon‐Web‐Services, along with Google, Microsoft, IBM, etc, offers resizable compute capacity in the cloud changing the economics of computing by eliminating the server fixed expenses. A cost‐efficient elastic cluster is available for a wide users community who can perform Monte Carlo simulations or other numerical applications using Amazon Machine Images. These virtual machines can be configured to work in a particular scientific environment, i.e. GEANT4 or PENELOPE. GEANT4 patient‐independent dose simulations were launched in parallel on EC2 using high CPU extra‐large spot instances (7 GB of memory, 8 virtual cores at 2.13 to 2.44GHz). Data storage was performed using the Simple Storage Service (S3). The S3 Organizer was used to upload the simulation or download multiple files. The 4‐layer EC2 security system, composed of the host/guest operating systems, Firewall, and signed secret access keys, ensures data privacy. Results: A local script was set‐up to launch multiple dose calculation instances on EC2 and automatically save the results on S3. Geant4‐application development, testing, and debugging were performed on a local workstation, along with data analysis and comparison with experiment. IAEA‐compliant patient‐independent PHSP files were generated and dose calculations were successfully validated against experiment. Conclusions: Cloud computing offers a low‐cost alternative for improved radiotherapy planning and optimization algorithms by enabling beam transport and Monte Carlo‐based dose calculations. Work supported by Varian Medical Systems