The DeepHealth HPC Infrastructure: Leveraging Heterogenous HPC and Cloud-Computing Infrastructures for IA-Based Medical Solutions

Eduardo Quiñones; Jesus Perales; Jorge Ejarque; Asaf Badouh; Santiago Marco; Fabrice Auzanneau; François Galea; David González; José Ramón Hervás; Tatiana Silva; Iacopo Colonnelli; Barbara Cantalupo; Marco Aldinucci; Enzo Tartaglione; Rafael Tornero; José Flich; Jose Maria Martínez; David Rodriguez; Izan Catalán; Jorge García; Carles Hernández

doi:10.1201/9781003176664-10

The DeepHealth HPC Infrastructure: Leveraging Heterogenous HPC and Cloud-Computing Infrastructures for IA-Based Medical Solutions

Eduardo Quiñones
, Jesus Perales
, Jorge Ejarque
, Asaf Badouh
, Santiago Marco
, Fabrice Auzanneau
, François Galea
, David González
, José Ramón Hervás
, Tatiana Silva
, Iacopo Colonnelli
, Barbara Cantalupo
, Marco Aldinucci
, Enzo Tartaglione
, Rafael Tornero
, José Flich
, Jose Maria Martínez
, David Rodriguez
, Izan Catalán
, Jorge García

Carles Hernández

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Abstract

This chapter presents the DeepHealth HPC toolkit for an efficient execution of deep learning (DL) medical application into HPC and cloud-computing infrastructures, featuring many-core, GPU, and FPGA acceleration devices. The toolkit offers to the European Computer Vision Library and the European Distributed Deep Learning Library (EDDL), developed in the DeepHealth project as well, the mechanisms to distribute and parallelize DL operations on HPC and cloud infrastructures in a fully transparent way. The toolkit implements workflow managers used to orchestrate HPC workloads for an efficient parallelization of EDDL training operations on HPC and cloud infrastructures, and includes the parallel programming models for an efficient execution EDDL inference and training operations on many-core, GPUs and FPGAs acceleration devices.

Original language	English
Title of host publication	HPC, Big Data, and AI Convergence Towards Exascale
Subtitle of host publication	Challenge and Vision
Publisher	CRC Press
Pages	191-216
Number of pages	26
ISBN (Electronic)	9781000485110
ISBN (Print)	9781032009841
DOIs	https://doi.org/10.1201/9781003176664-10
Publication status	Published - 1 Jan 2022
Externally published	Yes

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10.1201/9781003176664-10

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Quiñones, E., Perales, J., Ejarque, J., Badouh, A., Marco, S., Auzanneau, F., Galea, F., González, D., Hervás, J. R., Silva, T., Colonnelli, I., Cantalupo, B., Aldinucci, M., Tartaglione, E., Tornero, R., Flich, J., Martínez, J. M., Rodriguez, D., Catalán, I., ... Hernández, C. (2022). The DeepHealth HPC Infrastructure: Leveraging Heterogenous HPC and Cloud-Computing Infrastructures for IA-Based Medical Solutions. In HPC, Big Data, and AI Convergence Towards Exascale: Challenge and Vision (pp. 191-216). CRC Press. https://doi.org/10.1201/9781003176664-10

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title = "The DeepHealth HPC Infrastructure: Leveraging Heterogenous HPC and Cloud-Computing Infrastructures for IA-Based Medical Solutions",

abstract = "This chapter presents the DeepHealth HPC toolkit for an efficient execution of deep learning (DL) medical application into HPC and cloud-computing infrastructures, featuring many-core, GPU, and FPGA acceleration devices. The toolkit offers to the European Computer Vision Library and the European Distributed Deep Learning Library (EDDL), developed in the DeepHealth project as well, the mechanisms to distribute and parallelize DL operations on HPC and cloud infrastructures in a fully transparent way. The toolkit implements workflow managers used to orchestrate HPC workloads for an efficient parallelization of EDDL training operations on HPC and cloud infrastructures, and includes the parallel programming models for an efficient execution EDDL inference and training operations on many-core, GPUs and FPGAs acceleration devices.",

author = "Eduardo Qui{\~n}ones and Jesus Perales and Jorge Ejarque and Asaf Badouh and Santiago Marco and Fabrice Auzanneau and Fran{\c c}ois Galea and David Gonz{\'a}lez and Herv{\'a}s, \{Jos{\'e} Ram{\'o}n\} and Tatiana Silva and Iacopo Colonnelli and Barbara Cantalupo and Marco Aldinucci and Enzo Tartaglione and Rafael Tornero and Jos{\'e} Flich and Mart{\'i}nez, \{Jose Maria\} and David Rodriguez and Izan Catal{\'a}n and Jorge Garc{\'i}a and Carles Hern{\'a}ndez",

note = "Publisher Copyright: {\textcopyright} 2022 selection and editorial matter, Olivier Terzo and Jan Martinovic; individual chapters, the contributors.",

year = "2022",

month = jan,

day = "1",

doi = "10.1201/9781003176664-10",

language = "English",

isbn = "9781032009841",

pages = "191--216",

booktitle = "HPC, Big Data, and AI Convergence Towards Exascale",

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Quiñones, E, Perales, J, Ejarque, J, Badouh, A, Marco, S, Auzanneau, F, Galea, F, González, D, Hervás, JR, Silva, T, Colonnelli, I, Cantalupo, B, Aldinucci, M, Tartaglione, E, Tornero, R, Flich, J, Martínez, JM, Rodriguez, D, Catalán, I, García, J & Hernández, C 2022, The DeepHealth HPC Infrastructure: Leveraging Heterogenous HPC and Cloud-Computing Infrastructures for IA-Based Medical Solutions. in HPC, Big Data, and AI Convergence Towards Exascale: Challenge and Vision. CRC Press, pp. 191-216. https://doi.org/10.1201/9781003176664-10

The DeepHealth HPC Infrastructure: Leveraging Heterogenous HPC and Cloud-Computing Infrastructures for IA-Based Medical Solutions. / Quiñones, Eduardo; Perales, Jesus; Ejarque, Jorge et al.
HPC, Big Data, and AI Convergence Towards Exascale: Challenge and Vision. CRC Press, 2022. p. 191-216.

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

TY - CHAP

T1 - The DeepHealth HPC Infrastructure

T2 - Leveraging Heterogenous HPC and Cloud-Computing Infrastructures for IA-Based Medical Solutions

AU - Quiñones, Eduardo

AU - Perales, Jesus

AU - Ejarque, Jorge

AU - Badouh, Asaf

AU - Marco, Santiago

AU - Auzanneau, Fabrice

AU - Galea, François

AU - González, David

AU - Hervás, José Ramón

AU - Silva, Tatiana

AU - Colonnelli, Iacopo

AU - Cantalupo, Barbara

AU - Aldinucci, Marco

AU - Tartaglione, Enzo

AU - Tornero, Rafael

AU - Flich, José

AU - Martínez, Jose Maria

AU - Rodriguez, David

AU - Catalán, Izan

AU - García, Jorge

AU - Hernández, Carles

PY - 2022/1/1

Y1 - 2022/1/1

N2 - This chapter presents the DeepHealth HPC toolkit for an efficient execution of deep learning (DL) medical application into HPC and cloud-computing infrastructures, featuring many-core, GPU, and FPGA acceleration devices. The toolkit offers to the European Computer Vision Library and the European Distributed Deep Learning Library (EDDL), developed in the DeepHealth project as well, the mechanisms to distribute and parallelize DL operations on HPC and cloud infrastructures in a fully transparent way. The toolkit implements workflow managers used to orchestrate HPC workloads for an efficient parallelization of EDDL training operations on HPC and cloud infrastructures, and includes the parallel programming models for an efficient execution EDDL inference and training operations on many-core, GPUs and FPGAs acceleration devices.

AB - This chapter presents the DeepHealth HPC toolkit for an efficient execution of deep learning (DL) medical application into HPC and cloud-computing infrastructures, featuring many-core, GPU, and FPGA acceleration devices. The toolkit offers to the European Computer Vision Library and the European Distributed Deep Learning Library (EDDL), developed in the DeepHealth project as well, the mechanisms to distribute and parallelize DL operations on HPC and cloud infrastructures in a fully transparent way. The toolkit implements workflow managers used to orchestrate HPC workloads for an efficient parallelization of EDDL training operations on HPC and cloud infrastructures, and includes the parallel programming models for an efficient execution EDDL inference and training operations on many-core, GPUs and FPGAs acceleration devices.

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DO - 10.1201/9781003176664-10

M3 - Chapter

AN - SCOPUS:85138425254

SN - 9781032009841

SP - 191

EP - 216

BT - HPC, Big Data, and AI Convergence Towards Exascale

PB - CRC Press

ER -

The DeepHealth HPC Infrastructure: Leveraging Heterogenous HPC and Cloud-Computing Infrastructures for IA-Based Medical Solutions

Abstract

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