Evidence map›Paper›PMID 27558385›Full record

ArticleInternational journal of computer assisted radiology and surgery2017

Analytical modeling and feasibility study of a multi-GPU cloud-based server (MGCS) framework for non-voxel-based dose calculations.

J Neylon, Y Min, P Kupelian, D A Low, A Santhanam

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Article in International journal of computer assisted radiology and surgery, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

J NeylonDepartment of Radiation Oncology, University of California Los Angeles, 200 Medical Plaza, #B265, Los Angeles, CA, 90095, USA. jneylon@mednet.ucla.edu.
Y MinDepartment of Radiation Oncology, University of California Los Angeles, 200 Medical Plaza, #B265, Los Angeles, CA, 90095, USA.
P KupelianDepartment of Radiation Oncology, University of California Los Angeles, 200 Medical Plaza, #B265, Los Angeles, CA, 90095, USA.
D A LowDepartment of Radiation Oncology, University of California Los Angeles, 200 Medical Plaza, #B265, Los Angeles, CA, 90095, USA.
A SanthanamDepartment of Radiation Oncology, University of California Los Angeles, 200 Medical Plaza, #B265, Los Angeles, CA, 90095, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeIn this paper, a multi-GPU cloud-based server (MGCS) framework is presented for dose calculations, exploring the feasibility of remote computing power for parallelization and acceleration of computationally and time intensive radiotherapy tasks in moving toward online adaptive therapies.

methodsAn analytical model was developed to estimate theoretical MGCS performance acceleration and intelligently determine workload distribution. Numerical studies were performed with a computing setup of 14 GPUs distributed over 4 servers interconnected by a 1 Gigabits per second (Gbps) network. Inter-process communication methods were optimized to facilitate resource distribution and minimize data transfers over the server interconnect.

resultsThe analytically predicted computation time predicted matched experimentally observations within 1-5 %. MGCS performance approached a theoretical limit of acceleration proportional to the number of GPUs utilized when computational tasks far outweighed memory operations. The MGCS implementation reproduced ground-truth dose computations with negligible differences, by distributing the work among several processes and implemented optimization strategies.

conclusionsThe results showed that a cloud-based computation engine was a feasible solution for enabling clinics to make use of fast dose calculations for advanced treatment planning and adaptive radiotherapy. The cloud-based system was able to exceed the performance of a local machine even for optimized calculations, and provided significant acceleration for computationally intensive tasks. Such a framework can provide access to advanced technology and computational methods to many clinics, providing an avenue for standardization across institutions without the requirements of purchasing, maintaining, and continually updating hardware.

Indexed as

Cloud ComputingModels, TheoreticalRadiotherapy DosageAlgorithmsFeasibility StudiesHumansCloud computingGPUMulti-GPURadiotherapy

Identifiers

PMID27558385

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