Scaling the Distributed Stochastic Simulation to Exaflop Supercomputers
The Monte-Carlo method (stochastic simulation) is the one of the major tools in statistical physics, complex systems science and many other fields and is considered to be the promising computational scheme to run on nearest future exaflop supercomputers with many thousands and even millions of compu...
Gespeichert in:
| Hauptverfasser: | , , , , |
|---|---|
| Format: | Tagungsbericht |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 1136 |
|---|---|
| container_issue | |
| container_start_page | 1131 |
| container_title | |
| container_volume | |
| creator | Glinsky, B. Rodionov, A. Marchenko, M. Podkorytov, D. Weins, D. |
| description | The Monte-Carlo method (stochastic simulation) is the one of the major tools in statistical physics, complex systems science and many other fields and is considered to be the promising computational scheme to run on nearest future exaflop supercomputers with many thousands and even millions of computational cores. We suggest a technique of the distributed stochastic simulation suitable for running on large amount of computational cores of the supercomputer. An example of the highly scalable application utilizing distributed stochastic simulation on up-to-date tera- and petaflop supercomputers is the program library PARMONC. Thorough examination of the proposed technique was done using simulation model that is based on the multiagent simulation system AGNES. The AGNES in particular enables one to evaluate the performance of the supposed exaflop supercomputer loaded with the distributed stochastic simulation. |
| doi_str_mv | 10.1109/HPCC.2012.166 |
| format | Conference Proceeding |
| fullrecord | <record><control><sourceid>ieee_6IE</sourceid><recordid>TN_cdi_ieee_primary_6332301</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>6332301</ieee_id><sourcerecordid>6332301</sourcerecordid><originalsourceid>FETCH-LOGICAL-i175t-64c1436c9996564d7ab9055b909354250f307e3f4aa8ca6c5a02d6c10c22e3f23</originalsourceid><addsrcrecordid>eNotjM1Kw0AURkdE0NYuXbmZF0i8d37udJYSaysUFKLrMp1M7EjShGQC-vYGdPMdOBw-xu4QckSwD7u3osgFoMiR6IItwJDVyiirLtkCFRkpkNT6mq3G8QsAEKQGY27YtvSuiedPnk6BP8UxDfE4pVDxMnX-5MYUPS9jOzUuxe7MU8c3365uup6XUx8G37X9nA_jLbuqXTOG1T-X7ON5817ssv3r9qV43GcRjU4ZKY9KkrfWkiZVGXe0oPU8VmolNNQSTJC1cm7tHXntQFTkEbwQsxZyye7_fmMI4dAPsXXDz4GkFBJQ_gKr70s8</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Scaling the Distributed Stochastic Simulation to Exaflop Supercomputers</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>Glinsky, B. ; Rodionov, A. ; Marchenko, M. ; Podkorytov, D. ; Weins, D.</creator><creatorcontrib>Glinsky, B. ; Rodionov, A. ; Marchenko, M. ; Podkorytov, D. ; Weins, D.</creatorcontrib><description>The Monte-Carlo method (stochastic simulation) is the one of the major tools in statistical physics, complex systems science and many other fields and is considered to be the promising computational scheme to run on nearest future exaflop supercomputers with many thousands and even millions of computational cores. We suggest a technique of the distributed stochastic simulation suitable for running on large amount of computational cores of the supercomputer. An example of the highly scalable application utilizing distributed stochastic simulation on up-to-date tera- and petaflop supercomputers is the program library PARMONC. Thorough examination of the proposed technique was done using simulation model that is based on the multiagent simulation system AGNES. The AGNES in particular enables one to evaluate the performance of the supposed exaflop supercomputer loaded with the distributed stochastic simulation.</description><identifier>ISBN: 1467321648</identifier><identifier>ISBN: 9781467321648</identifier><identifier>EISBN: 0769547494</identifier><identifier>EISBN: 9780769547497</identifier><identifier>DOI: 10.1109/HPCC.2012.166</identifier><identifier>CODEN: IEEPAD</identifier><language>eng</language><publisher>IEEE</publisher><subject>Biological system modeling ; Computational modeling ; Data models ; distributed computing ; Generators ; Monte Carlo methods ; Multiagent systems ; parallel algorithms ; parallel architectures ; random number generation ; simulation ; Stochastic processes ; Supercomputers</subject><ispartof>2012 IEEE 14th International Conference on High Performance Computing and Communication & 2012 IEEE 9th International Conference on Embedded Software and Systems, 2012, p.1131-1136</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/6332301$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2058,27925,54920</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/6332301$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Glinsky, B.</creatorcontrib><creatorcontrib>Rodionov, A.</creatorcontrib><creatorcontrib>Marchenko, M.</creatorcontrib><creatorcontrib>Podkorytov, D.</creatorcontrib><creatorcontrib>Weins, D.</creatorcontrib><title>Scaling the Distributed Stochastic Simulation to Exaflop Supercomputers</title><title>2012 IEEE 14th International Conference on High Performance Computing and Communication & 2012 IEEE 9th International Conference on Embedded Software and Systems</title><addtitle>hpcc-icess</addtitle><description>The Monte-Carlo method (stochastic simulation) is the one of the major tools in statistical physics, complex systems science and many other fields and is considered to be the promising computational scheme to run on nearest future exaflop supercomputers with many thousands and even millions of computational cores. We suggest a technique of the distributed stochastic simulation suitable for running on large amount of computational cores of the supercomputer. An example of the highly scalable application utilizing distributed stochastic simulation on up-to-date tera- and petaflop supercomputers is the program library PARMONC. Thorough examination of the proposed technique was done using simulation model that is based on the multiagent simulation system AGNES. The AGNES in particular enables one to evaluate the performance of the supposed exaflop supercomputer loaded with the distributed stochastic simulation.</description><subject>Biological system modeling</subject><subject>Computational modeling</subject><subject>Data models</subject><subject>distributed computing</subject><subject>Generators</subject><subject>Monte Carlo methods</subject><subject>Multiagent systems</subject><subject>parallel algorithms</subject><subject>parallel architectures</subject><subject>random number generation</subject><subject>simulation</subject><subject>Stochastic processes</subject><subject>Supercomputers</subject><isbn>1467321648</isbn><isbn>9781467321648</isbn><isbn>0769547494</isbn><isbn>9780769547497</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2012</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotjM1Kw0AURkdE0NYuXbmZF0i8d37udJYSaysUFKLrMp1M7EjShGQC-vYGdPMdOBw-xu4QckSwD7u3osgFoMiR6IItwJDVyiirLtkCFRkpkNT6mq3G8QsAEKQGY27YtvSuiedPnk6BP8UxDfE4pVDxMnX-5MYUPS9jOzUuxe7MU8c3365uup6XUx8G37X9nA_jLbuqXTOG1T-X7ON5817ssv3r9qV43GcRjU4ZKY9KkrfWkiZVGXe0oPU8VmolNNQSTJC1cm7tHXntQFTkEbwQsxZyye7_fmMI4dAPsXXDz4GkFBJQ_gKr70s8</recordid><startdate>201206</startdate><enddate>201206</enddate><creator>Glinsky, B.</creator><creator>Rodionov, A.</creator><creator>Marchenko, M.</creator><creator>Podkorytov, D.</creator><creator>Weins, D.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201206</creationdate><title>Scaling the Distributed Stochastic Simulation to Exaflop Supercomputers</title><author>Glinsky, B. ; Rodionov, A. ; Marchenko, M. ; Podkorytov, D. ; Weins, D.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-64c1436c9996564d7ab9055b909354250f307e3f4aa8ca6c5a02d6c10c22e3f23</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Biological system modeling</topic><topic>Computational modeling</topic><topic>Data models</topic><topic>distributed computing</topic><topic>Generators</topic><topic>Monte Carlo methods</topic><topic>Multiagent systems</topic><topic>parallel algorithms</topic><topic>parallel architectures</topic><topic>random number generation</topic><topic>simulation</topic><topic>Stochastic processes</topic><topic>Supercomputers</topic><toplevel>online_resources</toplevel><creatorcontrib>Glinsky, B.</creatorcontrib><creatorcontrib>Rodionov, A.</creatorcontrib><creatorcontrib>Marchenko, M.</creatorcontrib><creatorcontrib>Podkorytov, D.</creatorcontrib><creatorcontrib>Weins, D.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Glinsky, B.</au><au>Rodionov, A.</au><au>Marchenko, M.</au><au>Podkorytov, D.</au><au>Weins, D.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Scaling the Distributed Stochastic Simulation to Exaflop Supercomputers</atitle><btitle>2012 IEEE 14th International Conference on High Performance Computing and Communication & 2012 IEEE 9th International Conference on Embedded Software and Systems</btitle><stitle>hpcc-icess</stitle><date>2012-06</date><risdate>2012</risdate><spage>1131</spage><epage>1136</epage><pages>1131-1136</pages><isbn>1467321648</isbn><isbn>9781467321648</isbn><eisbn>0769547494</eisbn><eisbn>9780769547497</eisbn><coden>IEEPAD</coden><abstract>The Monte-Carlo method (stochastic simulation) is the one of the major tools in statistical physics, complex systems science and many other fields and is considered to be the promising computational scheme to run on nearest future exaflop supercomputers with many thousands and even millions of computational cores. We suggest a technique of the distributed stochastic simulation suitable for running on large amount of computational cores of the supercomputer. An example of the highly scalable application utilizing distributed stochastic simulation on up-to-date tera- and petaflop supercomputers is the program library PARMONC. Thorough examination of the proposed technique was done using simulation model that is based on the multiagent simulation system AGNES. The AGNES in particular enables one to evaluate the performance of the supposed exaflop supercomputer loaded with the distributed stochastic simulation.</abstract><pub>IEEE</pub><doi>10.1109/HPCC.2012.166</doi><tpages>6</tpages></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISBN: 1467321648 |
| ispartof | 2012 IEEE 14th International Conference on High Performance Computing and Communication & 2012 IEEE 9th International Conference on Embedded Software and Systems, 2012, p.1131-1136 |
| issn | |
| language | eng |
| recordid | cdi_ieee_primary_6332301 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Biological system modeling Computational modeling Data models distributed computing Generators Monte Carlo methods Multiagent systems parallel algorithms parallel architectures random number generation simulation Stochastic processes Supercomputers |
| title | Scaling the Distributed Stochastic Simulation to Exaflop Supercomputers |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-29T18%3A12%3A01IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_6IE&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Scaling%20the%20Distributed%20Stochastic%20Simulation%20to%20Exaflop%20Supercomputers&rft.btitle=2012%20IEEE%2014th%20International%20Conference%20on%20High%20Performance%20Computing%20and%20Communication%20&%202012%20IEEE%209th%20International%20Conference%20on%20Embedded%20Software%20and%20Systems&rft.au=Glinsky,%20B.&rft.date=2012-06&rft.spage=1131&rft.epage=1136&rft.pages=1131-1136&rft.isbn=1467321648&rft.isbn_list=9781467321648&rft.coden=IEEPAD&rft_id=info:doi/10.1109/HPCC.2012.166&rft_dat=%3Cieee_6IE%3E6332301%3C/ieee_6IE%3E%3Curl%3E%3C/url%3E&rft.eisbn=0769547494&rft.eisbn_list=9780769547497&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=6332301&rfr_iscdi=true |