The DeepHealth HPC Infrastructure: Leveraging Heterogenous HPC and Cloud-Computing Infrastructures for IA-Based Medical Solutions
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 Dis...
Gespeichert in:
| Hauptverfasser: | , , , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Buchkapitel |
| Sprache: | eng |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 216 |
|---|---|
| container_issue | |
| container_start_page | 191 |
| container_title | |
| container_volume | 1 |
| creator | Quiñones, Eduardo Perales, Jesus Ejarque, Jorge Badouh, Asaf Marco, Santiago Auzanneau, Fabrice Galea, François González, David Hervás, José Ramón Silva, Tatiana Colonnelli, Iacopo Cantalupo, Barbara Aldinucci, Marco Tartaglione, Enzo Tornero, Rafael Flich, José Martínez, Jose Maria Rodriguez, David Catalán, Izan Hernández, Carles |
| description | 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. |
| doi_str_mv | 10.1201/9781003176664-10 |
| format | Book Chapter |
| fullrecord | <record><control><sourceid>proquest_infor</sourceid><recordid>TN_cdi_proquest_ebookcentralchapters_6839882_173_220</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>EBC6839882_173_220</sourcerecordid><originalsourceid>FETCH-LOGICAL-i180t-584382aea806bb7e2a5f44cde79b21c22c1f9ce175e0000280bc169a65fa0e0c3</originalsourceid><addsrcrecordid>eNpVkE1Lw0AQhldEUWvvHusPiM7sVzZHqR8pFPRQwduy2U5oMCZxd6v4702oF0_DM_A-zLyMXSHcIAe8LXKDAAJzrbXMEI7YxcggjcL87XgCwQEKI9XpCLxAgZJrdcbmMTYVKCHAoFbn7Hqzo8U90VCSa9NuUb4sF6uuDi6msPdpH-iSndSujTT_mzP2-viwWZbZ-vlptbxbZw0aSJkyUhjuyBnQVZUTd6qW0m8pLyqOnnOPdeEJc0XjocANVB514bSqHRB4MWPy4B1C_7mnmCxVff_uqUvBtX7nhkQhWm1EYQy3mAvLOYyx8hBruroPH-67D-3WJvfT9mF8o_NNnDTRItipOvuvumn7NWqbvuPiF3Z0YS0</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>book_chapter</recordtype><pqid>EBC6839882_173_220</pqid></control><display><type>book_chapter</type><title>The DeepHealth HPC Infrastructure: Leveraging Heterogenous HPC and Cloud-Computing Infrastructures for IA-Based Medical Solutions</title><source>O'Reilly Online Learning: Academic/Public Library Edition</source><creator>Quiñones, Eduardo ; Perales, Jesus ; Ejarque, Jorge ; Badouh, Asaf ; Marco, Santiago ; Auzanneau, Fabrice ; Galea, François ; González, David ; Hervás, José Ramón ; Silva, Tatiana ; Colonnelli, Iacopo ; Cantalupo, Barbara ; Aldinucci, Marco ; Tartaglione, Enzo ; Tornero, Rafael ; Flich, José ; Martínez, Jose Maria ; Rodriguez, David ; Catalán, Izan ; Hernández, Carles</creator><contributor>Terzo, Olivier ; Martinovič, Jan</contributor><creatorcontrib>Quiñones, Eduardo ; Perales, Jesus ; Ejarque, Jorge ; Badouh, Asaf ; Marco, Santiago ; Auzanneau, Fabrice ; Galea, François ; González, David ; Hervás, José Ramón ; Silva, Tatiana ; Colonnelli, Iacopo ; Cantalupo, Barbara ; Aldinucci, Marco ; Tartaglione, Enzo ; Tornero, Rafael ; Flich, José ; Martínez, Jose Maria ; Rodriguez, David ; Catalán, Izan ; Hernández, Carles ; Terzo, Olivier ; Martinovič, Jan</creatorcontrib><description>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.</description><edition>1</edition><identifier>ISBN: 1032009845</identifier><identifier>ISBN: 9781032009841</identifier><identifier>ISBN: 1032009918</identifier><identifier>ISBN: 9781032009919</identifier><identifier>EISBN: 100048517X</identifier><identifier>EISBN: 9781000485172</identifier><identifier>EISBN: 9781003176664</identifier><identifier>EISBN: 1000485110</identifier><identifier>EISBN: 9781000485110</identifier><identifier>EISBN: 1003176666</identifier><identifier>DOI: 10.1201/9781003176664-10</identifier><identifier>OCLC: 1291314265</identifier><identifier>LCCallNum: QA76 .H544 2022</identifier><language>eng</language><publisher>United Kingdom: Routledge</publisher><ispartof>HPC, Big Data, and AI Convergence Towards Exascale, 2022, Vol.1, p.191-216</ispartof><rights>2022 selection and editorial matter, Olivier Terzo and Jan Martinovicˇ; individual chapters, the contributors</rights><woscitedreferencessubscribed>false</woscitedreferencessubscribed><orcidid>0000-0001-7951-3914 ; 0000-0001-5393-3195 ; 0000-0003-4274-8298 ; 0000-0002-5465-964X ; 0000-0001-7575-3902 ; 0000-0001-8788-0829 ; 0000-0001-9290-2017 ; 0000-0003-4725-5097</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Uhttps://ebookcentral.proquest.com/covers/6839882-l.jpg</thumbnail><link.rule.ids>775,776,780,789,27902</link.rule.ids></links><search><contributor>Terzo, Olivier</contributor><contributor>Martinovič, Jan</contributor><creatorcontrib>Quiñones, Eduardo</creatorcontrib><creatorcontrib>Perales, Jesus</creatorcontrib><creatorcontrib>Ejarque, Jorge</creatorcontrib><creatorcontrib>Badouh, Asaf</creatorcontrib><creatorcontrib>Marco, Santiago</creatorcontrib><creatorcontrib>Auzanneau, Fabrice</creatorcontrib><creatorcontrib>Galea, François</creatorcontrib><creatorcontrib>González, David</creatorcontrib><creatorcontrib>Hervás, José Ramón</creatorcontrib><creatorcontrib>Silva, Tatiana</creatorcontrib><creatorcontrib>Colonnelli, Iacopo</creatorcontrib><creatorcontrib>Cantalupo, Barbara</creatorcontrib><creatorcontrib>Aldinucci, Marco</creatorcontrib><creatorcontrib>Tartaglione, Enzo</creatorcontrib><creatorcontrib>Tornero, Rafael</creatorcontrib><creatorcontrib>Flich, José</creatorcontrib><creatorcontrib>Martínez, Jose Maria</creatorcontrib><creatorcontrib>Rodriguez, David</creatorcontrib><creatorcontrib>Catalán, Izan</creatorcontrib><creatorcontrib>Hernández, Carles</creatorcontrib><title>The DeepHealth HPC Infrastructure: Leveraging Heterogenous HPC and Cloud-Computing Infrastructures for IA-Based Medical Solutions</title><title>HPC, Big Data, and AI Convergence Towards Exascale</title><description>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.</description><isbn>1032009845</isbn><isbn>9781032009841</isbn><isbn>1032009918</isbn><isbn>9781032009919</isbn><isbn>100048517X</isbn><isbn>9781000485172</isbn><isbn>9781003176664</isbn><isbn>1000485110</isbn><isbn>9781000485110</isbn><isbn>1003176666</isbn><fulltext>true</fulltext><rsrctype>book_chapter</rsrctype><creationdate>2022</creationdate><recordtype>book_chapter</recordtype><recordid>eNpVkE1Lw0AQhldEUWvvHusPiM7sVzZHqR8pFPRQwduy2U5oMCZxd6v4702oF0_DM_A-zLyMXSHcIAe8LXKDAAJzrbXMEI7YxcggjcL87XgCwQEKI9XpCLxAgZJrdcbmMTYVKCHAoFbn7Hqzo8U90VCSa9NuUb4sF6uuDi6msPdpH-iSndSujTT_mzP2-viwWZbZ-vlptbxbZw0aSJkyUhjuyBnQVZUTd6qW0m8pLyqOnnOPdeEJc0XjocANVB514bSqHRB4MWPy4B1C_7mnmCxVff_uqUvBtX7nhkQhWm1EYQy3mAvLOYyx8hBruroPH-67D-3WJvfT9mF8o_NNnDTRItipOvuvumn7NWqbvuPiF3Z0YS0</recordid><startdate>2022</startdate><enddate>2022</enddate><creator>Quiñones, Eduardo</creator><creator>Perales, Jesus</creator><creator>Ejarque, Jorge</creator><creator>Badouh, Asaf</creator><creator>Marco, Santiago</creator><creator>Auzanneau, Fabrice</creator><creator>Galea, François</creator><creator>González, David</creator><creator>Hervás, José Ramón</creator><creator>Silva, Tatiana</creator><creator>Colonnelli, Iacopo</creator><creator>Cantalupo, Barbara</creator><creator>Aldinucci, Marco</creator><creator>Tartaglione, Enzo</creator><creator>Tornero, Rafael</creator><creator>Flich, José</creator><creator>Martínez, Jose Maria</creator><creator>Rodriguez, David</creator><creator>Catalán, Izan</creator><creator>Hernández, Carles</creator><general>Routledge</general><general>Taylor & Francis Group</general><scope>FFUUA</scope><orcidid>https://orcid.org/0000-0001-7951-3914</orcidid><orcidid>https://orcid.org/0000-0001-5393-3195</orcidid><orcidid>https://orcid.org/0000-0003-4274-8298</orcidid><orcidid>https://orcid.org/0000-0002-5465-964X</orcidid><orcidid>https://orcid.org/0000-0001-7575-3902</orcidid><orcidid>https://orcid.org/0000-0001-8788-0829</orcidid><orcidid>https://orcid.org/0000-0001-9290-2017</orcidid><orcidid>https://orcid.org/0000-0003-4725-5097</orcidid></search><sort><creationdate>2022</creationdate><title>The DeepHealth HPC Infrastructure</title><author>Quiñones, Eduardo ; Perales, Jesus ; Ejarque, Jorge ; Badouh, Asaf ; Marco, Santiago ; Auzanneau, Fabrice ; Galea, François ; González, David ; Hervás, José Ramón ; Silva, Tatiana ; Colonnelli, Iacopo ; Cantalupo, Barbara ; Aldinucci, Marco ; Tartaglione, Enzo ; Tornero, Rafael ; Flich, José ; Martínez, Jose Maria ; Rodriguez, David ; Catalán, Izan ; Hernández, Carles</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i180t-584382aea806bb7e2a5f44cde79b21c22c1f9ce175e0000280bc169a65fa0e0c3</frbrgroupid><rsrctype>book_chapters</rsrctype><prefilter>book_chapters</prefilter><language>eng</language><creationdate>2022</creationdate><toplevel>online_resources</toplevel><creatorcontrib>Quiñones, Eduardo</creatorcontrib><creatorcontrib>Perales, Jesus</creatorcontrib><creatorcontrib>Ejarque, Jorge</creatorcontrib><creatorcontrib>Badouh, Asaf</creatorcontrib><creatorcontrib>Marco, Santiago</creatorcontrib><creatorcontrib>Auzanneau, Fabrice</creatorcontrib><creatorcontrib>Galea, François</creatorcontrib><creatorcontrib>González, David</creatorcontrib><creatorcontrib>Hervás, José Ramón</creatorcontrib><creatorcontrib>Silva, Tatiana</creatorcontrib><creatorcontrib>Colonnelli, Iacopo</creatorcontrib><creatorcontrib>Cantalupo, Barbara</creatorcontrib><creatorcontrib>Aldinucci, Marco</creatorcontrib><creatorcontrib>Tartaglione, Enzo</creatorcontrib><creatorcontrib>Tornero, Rafael</creatorcontrib><creatorcontrib>Flich, José</creatorcontrib><creatorcontrib>Martínez, Jose Maria</creatorcontrib><creatorcontrib>Rodriguez, David</creatorcontrib><creatorcontrib>Catalán, Izan</creatorcontrib><creatorcontrib>Hernández, Carles</creatorcontrib><collection>ProQuest Ebook Central - Book Chapters - Demo use only</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Quiñones, Eduardo</au><au>Perales, Jesus</au><au>Ejarque, Jorge</au><au>Badouh, Asaf</au><au>Marco, Santiago</au><au>Auzanneau, Fabrice</au><au>Galea, François</au><au>González, David</au><au>Hervás, José Ramón</au><au>Silva, Tatiana</au><au>Colonnelli, Iacopo</au><au>Cantalupo, Barbara</au><au>Aldinucci, Marco</au><au>Tartaglione, Enzo</au><au>Tornero, Rafael</au><au>Flich, José</au><au>Martínez, Jose Maria</au><au>Rodriguez, David</au><au>Catalán, Izan</au><au>Hernández, Carles</au><au>Terzo, Olivier</au><au>Martinovič, Jan</au><format>book</format><genre>bookitem</genre><ristype>CHAP</ristype><atitle>The DeepHealth HPC Infrastructure: Leveraging Heterogenous HPC and Cloud-Computing Infrastructures for IA-Based Medical Solutions</atitle><btitle>HPC, Big Data, and AI Convergence Towards Exascale</btitle><date>2022</date><risdate>2022</risdate><volume>1</volume><spage>191</spage><epage>216</epage><pages>191-216</pages><isbn>1032009845</isbn><isbn>9781032009841</isbn><isbn>1032009918</isbn><isbn>9781032009919</isbn><eisbn>100048517X</eisbn><eisbn>9781000485172</eisbn><eisbn>9781003176664</eisbn><eisbn>1000485110</eisbn><eisbn>9781000485110</eisbn><eisbn>1003176666</eisbn><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.</abstract><cop>United Kingdom</cop><pub>Routledge</pub><doi>10.1201/9781003176664-10</doi><oclcid>1291314265</oclcid><tpages>26</tpages><edition>1</edition><orcidid>https://orcid.org/0000-0001-7951-3914</orcidid><orcidid>https://orcid.org/0000-0001-5393-3195</orcidid><orcidid>https://orcid.org/0000-0003-4274-8298</orcidid><orcidid>https://orcid.org/0000-0002-5465-964X</orcidid><orcidid>https://orcid.org/0000-0001-7575-3902</orcidid><orcidid>https://orcid.org/0000-0001-8788-0829</orcidid><orcidid>https://orcid.org/0000-0001-9290-2017</orcidid><orcidid>https://orcid.org/0000-0003-4725-5097</orcidid></addata></record> |
| fulltext | fulltext |
| identifier | ISBN: 1032009845 |
| ispartof | HPC, Big Data, and AI Convergence Towards Exascale, 2022, Vol.1, p.191-216 |
| issn | |
| language | eng |
| recordid | cdi_proquest_ebookcentralchapters_6839882_173_220 |
| source | O'Reilly Online Learning: Academic/Public Library Edition |
| title | The DeepHealth HPC Infrastructure: Leveraging Heterogenous HPC and Cloud-Computing Infrastructures for IA-Based Medical Solutions |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-31T19%3A05%3A52IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_infor&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=bookitem&rft.atitle=The%20DeepHealth%20HPC%20Infrastructure:%20Leveraging%20Heterogenous%20HPC%20and%20Cloud-Computing%20Infrastructures%20for%20IA-Based%20Medical%20Solutions&rft.btitle=HPC,%20Big%20Data,%20and%20AI%20Convergence%20Towards%20Exascale&rft.au=Qui%C3%B1ones,%20Eduardo&rft.date=2022&rft.volume=1&rft.spage=191&rft.epage=216&rft.pages=191-216&rft.isbn=1032009845&rft.isbn_list=9781032009841&rft.isbn_list=1032009918&rft.isbn_list=9781032009919&rft_id=info:doi/10.1201/9781003176664-10&rft_dat=%3Cproquest_infor%3EEBC6839882_173_220%3C/proquest_infor%3E%3Curl%3E%3C/url%3E&rft.eisbn=100048517X&rft.eisbn_list=9781000485172&rft.eisbn_list=9781003176664&rft.eisbn_list=1000485110&rft.eisbn_list=9781000485110&rft.eisbn_list=1003176666&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=EBC6839882_173_220&rft_id=info:pmid/&rfr_iscdi=true |