Distributed data structure templates for data‐intensive remote sensing applications
Identifieur interne : 000418 ( Istex/Corpus ); précédent : 000417; suivant : 000419Distributed data structure templates for data‐intensive remote sensing applications
Auteurs : Yan Ma ; Lizhe Wang ; Dingsheng Liu ; Tao Yuan ; Peng Liu ; Wanfeng ZhangSource :
- Concurrency and Computation: Practice and Experience [ 1532-0626 ] ; 2013-08-25.
Abstract
The remotely sensed images continuously acquired by satellite and airborne sensors are increasing dramatically. Remote sensing applications are overwhelmed with tons of remote sensing data with complex data structures. Efficient programming in parallel systems for data‐intensive applications like massive remote sensing data processing will be a challenge. We propose a generic data‐structure oriented programming template to support massive remote sensing data processing in high‐performance clusters. These templates provide distributed abstractions for large remote sensing image data with complex data structure and allow these distributed data to be accessed as a global one. Through data serialization and one‐sided message passing primitives provided by message passing interface, the distributed remote sensing data template whose sliced data blocks are scattered among nodes could offer a simple and effective way to distribute and communicate massive remote sensing data. Efficient parallel input/output directly to and from the distributed data structure will also be offered to address the input/output bottleneck caused by massive image data. Developers can take the advantage of our templates to program efficient parallel remote sensing algorithms without dealing with data slicing and communication through low‐level message passing interface APIs. Through experiments on remote sensing applications, we confirmed that our templates were productive and efficient. Copyright © 2012 John Wiley & Sons, Ltd.
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DOI: 10.1002/cpe.2965
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Lizhe Wang, Center for Earth Observation and Digital Earth, Chinese Academy of Sciences, China.E‐mail:</mods:affiliation></affiliation><affiliation><mods:affiliation>E-mail: lizhe.wang@gmail.com</mods:affiliation></affiliation></author><author><name sortKey="Liu, Dingsheng" sort="Liu, Dingsheng" uniqKey="Liu D" first="Dingsheng" last="Liu">Dingsheng Liu</name><affiliation><mods:affiliation>Center for Earth Observation and Digital Earth, Chinese Academy of Sciences, China</mods:affiliation></affiliation></author><author><name sortKey="Yuan, Tao" sort="Yuan, Tao" uniqKey="Yuan T" first="Tao" last="Yuan">Tao Yuan</name><affiliation><mods:affiliation>Center for Earth Observation and Digital Earth, Chinese Academy of Sciences, China</mods:affiliation></affiliation><affiliation><mods:affiliation>Graduate University of Chinese Academy of Sciences, China</mods:affiliation></affiliation></author><author><name sortKey="Liu, Peng" sort="Liu, Peng" uniqKey="Liu P" first="Peng" last="Liu">Peng 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China</mods:affiliation></affiliation><affiliation><mods:affiliation>Correspondence to: Lizhe Wang, Center for Earth Observation and Digital Earth, Chinese Academy of Sciences, China.E‐mail:</mods:affiliation></affiliation><affiliation><mods:affiliation>E-mail: lizhe.wang@gmail.com</mods:affiliation></affiliation></author><author><name sortKey="Liu, Dingsheng" sort="Liu, Dingsheng" uniqKey="Liu D" first="Dingsheng" last="Liu">Dingsheng Liu</name><affiliation><mods:affiliation>Center for Earth Observation and Digital Earth, Chinese Academy of Sciences, China</mods:affiliation></affiliation></author><author><name sortKey="Yuan, Tao" sort="Yuan, Tao" uniqKey="Yuan T" first="Tao" last="Yuan">Tao Yuan</name><affiliation><mods:affiliation>Center for Earth Observation and Digital Earth, Chinese Academy of Sciences, China</mods:affiliation></affiliation><affiliation><mods:affiliation>Graduate University of Chinese Academy of Sciences, China</mods:affiliation></affiliation></author><author><name sortKey="Liu, Peng" sort="Liu, Peng" uniqKey="Liu P" first="Peng" last="Liu">Peng Liu</name><affiliation><mods:affiliation>Center for Earth Observation and Digital Earth, Chinese Academy of Sciences, China</mods:affiliation></affiliation></author><author><name sortKey="Zhang, Wanfeng" sort="Zhang, Wanfeng" uniqKey="Zhang W" first="Wanfeng" last="Zhang">Wanfeng Zhang</name><affiliation><mods:affiliation>Center for Earth Observation and Digital Earth, Chinese Academy of Sciences, China</mods:affiliation></affiliation><affiliation><mods:affiliation>Graduate University of Chinese Academy of Sciences, China</mods:affiliation></affiliation></author></analytic><monogr></monogr><series><title level="j">Concurrency and Computation: Practice and Experience</title><title level="j" type="abbrev">Concurrency Computat.: Pract. Exper.</title><idno type="ISSN">1532-0626</idno><idno type="eISSN">1532-0634</idno><imprint><publisher>Blackwell Publishing Ltd</publisher><date type="published" when="2013-08-25">2013-08-25</date><biblScope unit="volume">25</biblScope><biblScope unit="issue">12</biblScope><biblScope unit="page" from="1784">1784</biblScope><biblScope unit="page" to="1797">1797</biblScope></imprint><idno type="ISSN">1532-0626</idno></series><idno type="istex">6B02BA7B32D997BB4BE8B5F238D7607ECDB8CDAB</idno><idno type="DOI">10.1002/cpe.2965</idno><idno type="ArticleID">CPE2965</idno></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">1532-0626</idno></seriesStmt></fileDesc><profileDesc><textClass></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract">The remotely sensed images continuously acquired by satellite and airborne sensors are increasing dramatically. Remote sensing applications are overwhelmed with tons of remote sensing data with complex data structures. Efficient programming in parallel systems for data‐intensive applications like massive remote sensing data processing will be a challenge. We propose a generic data‐structure oriented programming template to support massive remote sensing data processing in high‐performance clusters. These templates provide distributed abstractions for large remote sensing image data with complex data structure and allow these distributed data to be accessed as a global one. Through data serialization and one‐sided message passing primitives provided by message passing interface, the distributed remote sensing data template whose sliced data blocks are scattered among nodes could offer a simple and effective way to distribute and communicate massive remote sensing data. Efficient parallel input/output directly to and from the distributed data structure will also be offered to address the input/output bottleneck caused by massive image data. Developers can take the advantage of our templates to program efficient parallel remote sensing algorithms without dealing with data slicing and communication through low‐level message passing interface APIs. Through experiments on remote sensing applications, we confirmed that our templates were productive and efficient. Copyright © 2012 John Wiley & Sons, Ltd.</div></front></TEI><istex><corpusName>wiley</corpusName><author><json:item><name>Yan Ma</name><affiliations><json:string>Center for Earth Observation and Digital Earth, Chinese Academy of Sciences, China</json:string><json:string>Institute of Electronics, Chinese Academy of Sciences, China</json:string><json:string>Graduate University of Chinese Academy of Sciences, China</json:string></affiliations></json:item><json:item><name>Lizhe Wang</name><affiliations><json:string>Center for Earth Observation and Digital Earth, Chinese Academy of Sciences, China</json:string><json:string>Correspondence to: Lizhe Wang, Center for Earth Observation and Digital Earth, Chinese Academy of Sciences, China.E‐mail:</json:string><json:string>E-mail: lizhe.wang@gmail.com</json:string></affiliations></json:item><json:item><name>Dingsheng Liu</name><affiliations><json:string>Center for Earth Observation and Digital Earth, Chinese Academy of Sciences, China</json:string></affiliations></json:item><json:item><name>Tao Yuan</name><affiliations><json:string>Center for Earth Observation and Digital Earth, Chinese Academy of Sciences, China</json:string><json:string>Graduate University of Chinese Academy of Sciences, China</json:string></affiliations></json:item><json:item><name>Peng Liu</name><affiliations><json:string>Center for Earth Observation and Digital Earth, Chinese Academy of Sciences, China</json:string></affiliations></json:item><json:item><name>Wanfeng Zhang</name><affiliations><json:string>Center for Earth Observation and Digital Earth, Chinese Academy of Sciences, China</json:string><json:string>Graduate University of Chinese Academy of Sciences, China</json:string></affiliations></json:item></author><subject><json:item><lang><json:string>eng</json:string></lang><value>parallel programming</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>generic programming</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>data‐intensive computing</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>remote sensing image processing</value></json:item></subject><articleId><json:string>CPE2965</json:string></articleId><language><json:string>eng</json:string></language><originalGenre><json:string>article</json:string></originalGenre><abstract>The remotely sensed images continuously acquired by satellite and airborne sensors are increasing dramatically. Remote sensing applications are overwhelmed with tons of remote sensing data with complex data structures. Efficient programming in parallel systems for data‐intensive applications like massive remote sensing data processing will be a challenge. We propose a generic data‐structure oriented programming template to support massive remote sensing data processing in high‐performance clusters. These templates provide distributed abstractions for large remote sensing image data with complex data structure and allow these distributed data to be accessed as a global one. Through data serialization and one‐sided message passing primitives provided by message passing interface, the distributed remote sensing data template whose sliced data blocks are scattered among nodes could offer a simple and effective way to distribute and communicate massive remote sensing data. Efficient parallel input/output directly to and from the distributed data structure will also be offered to address the input/output bottleneck caused by massive image data. Developers can take the advantage of our templates to program efficient parallel remote sensing algorithms without dealing with data slicing and communication through low‐level message passing interface APIs. Through experiments on remote sensing applications, we confirmed that our templates were productive and efficient. 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Remote sensing applications are overwhelmed with tons of remote sensing data with complex data structures. Efficient programming in parallel systems for data‐intensive applications like massive remote sensing data processing will be a challenge. We propose a generic data‐structure oriented programming template to support massive remote sensing data processing in high‐performance clusters. These templates provide distributed abstractions for large remote sensing image data with complex data structure and allow these distributed data to be accessed as a global one. Through data serialization and one‐sided message passing primitives provided by message passing interface, the distributed remote sensing data template whose sliced data blocks are scattered among nodes could offer a simple and effective way to distribute and communicate massive remote sensing data. Efficient parallel input/output directly to and from the distributed data structure will also be offered to address the input/output bottleneck caused by massive image data. Developers can take the advantage of our templates to program efficient parallel remote sensing algorithms without dealing with data slicing and communication through low‐level message passing interface APIs. Through experiments on remote sensing applications, we confirmed that our templates were productive and efficient. 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MA <i>ET AL.</i></title></titleGroup><creators><creator creatorRole="author" xml:id="cpe2965-cr-0008" affiliationRef="#cpe2965-aff-0001 #cpe2965-aff-0002 #cpe2965-aff-0003"><personName><givenNames>Yan</givenNames><familyName>Ma</familyName></personName></creator><creator creatorRole="author" xml:id="cpe2965-cr-0009" affiliationRef="#cpe2965-aff-0001" corresponding="yes"><personName><givenNames>Lizhe</givenNames><familyName>Wang</familyName></personName></creator><creator creatorRole="author" xml:id="cpe2965-cr-0010" affiliationRef="#cpe2965-aff-0001"><personName><givenNames>Dingsheng</givenNames><familyName>Liu</familyName></personName></creator><creator creatorRole="author" xml:id="cpe2965-cr-0011" affiliationRef="#cpe2965-aff-0001 #cpe2965-aff-0003"><personName><givenNames>Tao</givenNames><familyName>Yuan</familyName></personName></creator><creator creatorRole="author" xml:id="cpe2965-cr-0012" affiliationRef="#cpe2965-aff-0001"><personName><givenNames>Peng</givenNames><familyName>Liu</familyName></personName></creator><creator creatorRole="author" xml:id="cpe2965-cr-0013" affiliationRef="#cpe2965-aff-0001 #cpe2965-aff-0003"><personName><givenNames>Wanfeng</givenNames><familyName>Zhang</familyName></personName></creator></creators><affiliationGroup><affiliation countryCode="CN" type="organization" xml:id="cpe2965-aff-0001"><orgDiv>Center for Earth Observation and Digital Earth</orgDiv><orgName>Chinese Academy of Sciences</orgName><address><country>China</country></address></affiliation><affiliation countryCode="CN" type="organization" xml:id="cpe2965-aff-0002"><orgDiv>Institute of Electronics</orgDiv><orgName>Chinese Academy of Sciences</orgName><address><country>China</country></address></affiliation><affiliation countryCode="CN" type="organization" xml:id="cpe2965-aff-0003"><orgName>Graduate University of Chinese Academy of Sciences</orgName><address><country>China</country></address></affiliation></affiliationGroup><keywordGroup type="author"><keyword xml:id="cpe2965-kwd-0001">parallel programming</keyword><keyword xml:id="cpe2965-kwd-0002">generic programming</keyword><keyword xml:id="cpe2965-kwd-0003">data‐intensive computing</keyword><keyword xml:id="cpe2965-kwd-0004">remote sensing image processing</keyword></keywordGroup><abstractGroup><abstract type="main"><title type="main">SUMMARY</title><p>The remotely sensed images continuously acquired by satellite and airborne sensors are increasing dramatically. Remote sensing applications are overwhelmed with tons of remote sensing data with complex data structures. Efficient programming in parallel systems for data‐intensive applications like massive remote sensing data processing will be a challenge. We propose a generic data‐structure oriented programming template to support massive remote sensing data processing in high‐performance clusters. These templates provide distributed abstractions for large remote sensing image data with complex data structure and allow these distributed data to be accessed as a global one. Through data serialization and one‐sided message passing primitives provided by message passing interface, the distributed remote sensing data template whose sliced data blocks are scattered among nodes could offer a simple and effective way to distribute and communicate massive remote sensing data. Efficient parallel input/output directly to and from the distributed data structure will also be offered to address the input/output bottleneck caused by massive image data. Developers can take the advantage of our templates to program efficient parallel remote sensing algorithms without dealing with data slicing and communication through low‐level message passing interface APIs. Through experiments on remote sensing applications, we confirmed that our templates were productive and efficient. Copyright © 2012 John Wiley & Sons, Ltd.</p></abstract></abstractGroup></contentMeta></header></component></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Distributed data structure templates for data‐intensive remote sensing applications</title></titleInfo><titleInfo type="abbreviated" lang="en"><title>DISTRIBUTED DATA STRUCTURE TEMPLATES</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>Distributed data structure templates for data‐intensive remote sensing applications</title></titleInfo><name type="personal"><namePart type="given">Yan</namePart><namePart type="family">Ma</namePart><affiliation>Center for Earth Observation and Digital Earth, Chinese Academy of Sciences, China</affiliation><affiliation>Institute of Electronics, Chinese Academy of Sciences, China</affiliation><affiliation>Graduate University of Chinese Academy of Sciences, China</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Lizhe</namePart><namePart type="family">Wang</namePart><affiliation>Center for Earth Observation and Digital Earth, Chinese Academy of Sciences, China</affiliation><affiliation>Correspondence to: Lizhe Wang, Center for Earth Observation and Digital Earth, Chinese Academy of Sciences, China.E‐mail:</affiliation><affiliation>E-mail: lizhe.wang@gmail.com</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Dingsheng</namePart><namePart type="family">Liu</namePart><affiliation>Center for Earth Observation and Digital Earth, Chinese Academy of Sciences, China</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Tao</namePart><namePart type="family">Yuan</namePart><affiliation>Center for Earth Observation and Digital Earth, Chinese Academy of Sciences, China</affiliation><affiliation>Graduate University of Chinese Academy of Sciences, China</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Peng</namePart><namePart type="family">Liu</namePart><affiliation>Center for Earth Observation and Digital Earth, Chinese Academy of Sciences, China</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Wanfeng</namePart><namePart type="family">Zhang</namePart><affiliation>Center for Earth Observation and Digital Earth, Chinese Academy of Sciences, China</affiliation><affiliation>Graduate University of Chinese Academy of Sciences, China</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="article" displayLabel="article"></genre><originInfo><publisher>Blackwell Publishing Ltd</publisher><dateIssued encoding="w3cdtf">2013-08-25</dateIssued><dateCreated encoding="w3cdtf">2012-11-19</dateCreated><dateCaptured encoding="w3cdtf">2012-06-12</dateCaptured><dateValid encoding="w3cdtf">2012-10-31</dateValid><copyrightDate encoding="w3cdtf">2013</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><physicalDescription><internetMediaType>text/html</internetMediaType></physicalDescription><abstract>The remotely sensed images continuously acquired by satellite and airborne sensors are increasing dramatically. Remote sensing applications are overwhelmed with tons of remote sensing data with complex data structures. Efficient programming in parallel systems for data‐intensive applications like massive remote sensing data processing will be a challenge. We propose a generic data‐structure oriented programming template to support massive remote sensing data processing in high‐performance clusters. These templates provide distributed abstractions for large remote sensing image data with complex data structure and allow these distributed data to be accessed as a global one. Through data serialization and one‐sided message passing primitives provided by message passing interface, the distributed remote sensing data template whose sliced data blocks are scattered among nodes could offer a simple and effective way to distribute and communicate massive remote sensing data. Efficient parallel input/output directly to and from the distributed data structure will also be offered to address the input/output bottleneck caused by massive image data. Developers can take the advantage of our templates to program efficient parallel remote sensing algorithms without dealing with data slicing and communication through low‐level message passing interface APIs. Through experiments on remote sensing applications, we confirmed that our templates were productive and efficient. Copyright © 2012 John Wiley & Sons, Ltd.</abstract><subject><genre>keywords</genre><topic>parallel programming</topic><topic>generic programming</topic><topic>data‐intensive computing</topic><topic>remote sensing image processing</topic></subject><relatedItem type="host"><titleInfo><title>Concurrency and Computation: Practice and Experience</title></titleInfo><titleInfo type="abbreviated"><title>Concurrency Computat.: Pract. Exper.</title></titleInfo><name type="personal"><namePart type="given">Luis Miguel</namePart><namePart type="family">Vaquero</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Luis</namePart><namePart type="family">Rodero‐Merino</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Rajkumar</namePart><namePart type="family">Buyya</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Joanna</namePart><namePart type="family">Kołodziej</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Samee Ullah</namePart><namePart type="family">Khan</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Erol</namePart><namePart type="family">Gelenbe</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">El‐Ghazali</namePart><namePart type="family">Talbi</namePart><role><roleTerm type="text">editor</roleTerm></role></name><genre type="journal">journal</genre><subject><genre>article-category</genre><topic>Special Issue Paper</topic></subject><identifier type="ISSN">1532-0626</identifier><identifier type="eISSN">1532-0634</identifier><identifier type="DOI">10.1002/(ISSN)1532-0634</identifier><identifier type="PublisherID">CPE</identifier><part><date>2013</date><detail type="title"><title>Combined Special Issues on Cloud scalability: building the Millennium Falcon and Scalable optimization in grid, cloud, and intelligent network computing</title></detail><detail type="volume"><caption>vol.</caption><number>25</number></detail><detail type="issue"><caption>no.</caption><number>12</number></detail><extent unit="pages"><start>1784</start><end>1797</end><total>14</total></extent></part></relatedItem><identifier type="istex">6B02BA7B32D997BB4BE8B5F238D7607ECDB8CDAB</identifier><identifier type="DOI">10.1002/cpe.2965</identifier><identifier type="ArticleID">CPE2965</identifier><accessCondition type="use and reproduction" contentType="copyright">Copyright © 2013 John Wiley & Sons, Ltd.Copyright © 2012 John Wiley & Sons, Ltd.</accessCondition><recordInfo><recordContentSource>WILEY</recordContentSource></recordInfo></mods></metadata><enrichments><json:item><type>multicat</type><uri>https://api.istex.fr/document/6B02BA7B32D997BB4BE8B5F238D7607ECDB8CDAB/enrichments/multicat</uri></json:item></enrichments><serie></serie></istex></record>Pour manipuler ce document sous Unix (Dilib)
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