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Improving the Execution Performance of FreeSurfer

A New Scheduled Pipeline Scheme for Optimizing the Use of CPU and GPU Resources

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Abstract

A scheme to significantly speed up the processing of MRI with FreeSurfer (FS) is presented. The scheme is aimed at maximizing the productivity (number of subjects processed per unit time) for the use case of research projects with datasets involving many acquisitions. The scheme combines the already existing GPU-accelerated version of the FS workflow with a task-level parallel scheme supervised by a resource scheduler. This allows for an optimum utilization of the computational power of a given hardware platform while avoiding problems with shortages of platform resources. The scheme can be executed on a wide variety of platforms, as its implementation only involves the script that orchestrates the execution of the workflow components and the FS code itself requires no modifications. The scheme has been implemented and tested on a commodity platform within the reach of most research groups (a personal computer with four cores and an NVIDIA GeForce 480 GTX graphics card). Using the scheduled task-level parallel scheme, a productivity above 0.6 subjects per hour is achieved on the test platform, corresponding to a speedup of over six times compared to the default CPU-only serial FS workflow.

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Acknowledgments

This research has been supported by INNDACYT Association, partly funded by the Talent Empresa program of the AGAUR Agency, Knowledge and Economy Department of Generalitat de Catalunya and also MICINN-Spain under contract TIN2011-28689-C02-01.

Our work was possible thanks to the cooperation of INNDACYT Association with Port d’Informació Científica (PIC), and Hospital de la Santa Creu i Sant Pau (Barcelona). The Port d’Informació Científica (PIC) is maintained through a collaboration agreement between the Generalitat de Catalunya, CIEMAT, IFAE and the Universitat Autònoma de Barcelona.

We thank specially Richard Edgar, Bruce Fischl et al. from Athinoula A. Martinos Center for Biomedical Imaging (Harvard-MIT) for the offered support.

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Authors and Affiliations

  1. Asociación para la Innovación en Análisis, Gestión y Procesamiento de Datos Científicos y Tecnológicos (INNDACYT), Avda. Europa, 20, Planta Baja Puerta D, 08907, Hospitalet de Llobregat (L’), Barcelona, Spain

    J. Delgado

  2. Computer Architecture and Operating Systems (CAOS), Universitat Autònoma de Barcelona, Edifici Q, 08193, Bellaterra, Barcelona, Spain

    J. Delgado, J. C. Moure & A. Espinosa

  3. Port d’Informació Científica (PIC), Universitat Autònoma de Barcelona, Edifici D, 08193, Bellaterra, Barcelona, Spain

    Y. Vives-Gilabert & M. Delfino

  4. Institut de Física d’Altes Energies (IFAE), Universitat Autònoma de Barcelona, Edifici Cn, 08193, Bellaterra, Barcelona, Spain

    Y. Vives-Gilabert

  5. Department of Physics, Universitat Autònoma de Barcelona, 08193, Bellaterra, Barcelona, Spain

    M. Delfino

  6. Hospital de la Santa Creu i Sant Pau Barcelona, Sant Antoni Maria Claret 167, 08025, Barcelona, Spain

    B. Gómez-Ansón

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  1. J. Delgado
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  2. J. C. Moure
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  3. Y. Vives-Gilabert
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  4. M. Delfino
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  5. A. Espinosa
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  6. B. Gómez-Ansón
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Correspondence to J. Delgado.

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Delgado, J., Moure, J.C., Vives-Gilabert, Y. et al. Improving the Execution Performance of FreeSurfer. Neuroinform 12, 413–421 (2014). https://doi.org/10.1007/s12021-013-9214-1

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  • DOI: https://doi.org/10.1007/s12021-013-9214-1

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