mycoreobjectrosdok_document_0000016057rosdok_document_0000016057rosdokdocumentrosdok_derivate_0000086110rosdok_derivate_00000861112023-08-08T12:04:15.681Z2020-03-27T08:50:06.762ZadministratoreditorPpublished2rosdok_derivate_0000086110rosdok_derivate_0000086111truetruederivate_types:dataDatendatawf_edit_data wf_register_dataderivate_types:documentationDokumentationdocumentationwf_edit_data wf_register_datastate:publishedstate:publishedveröffentlichtpublished1697803288Oau2020-05-112023-08-05T16:41:46ZrdaConverted from PICA to MODS using Pica2Mods XSLT Transformer 2.7 [SCM: "0c0e7a3c226a4a0cbcbec39b493c3c5257339ab8" "v2.7" "2023-08-04T00:00:00+0200"] with mode 'DEFAULT'.rosdok/id00002648DatenpublikationForschungsdatenPartial evaluation via code generation for static stochastic reaction network models (Software Appendix)[research data]Succinct, declarative, and domain-specific modeling languages have many advantages when creating simulation models. However, it is often challenging to efficiently execute models defined in such languages. We use code generation for model-specific simulators. Code generation has been successfully applied for high-performance algorithms in many application domains. By generating tailored simulators for specific simulation models defined in a domain specific language, we get the best of both worlds: a succinct, declarative and formal presentation of the model and an efficient execution. We illustrate this based on a simple domain-specific language for biochemical reaction networks as well as on the network representation of the established BioNetGen language. We implement two approaches adopting the same simulation algorithms: one generic simulator that parses models at runtime and one generator that produces a simulator specialized to a given model based on partial evaluation and code generation. Akin to profile-guided optimization we also use dynamic execution of the model to further optimize the simulators. The performance of the approaches is carefully benchmarked using representative models of small to mid-sized biochemical reaction networks. The generic simulator achieves a performance similar to state of the art simulators in the domain, whereas the specialized simulator outperforms established simulation algorithms with a speedup of more than an order of magnitude. This repository contains the code generation software as described in the 2020 PADS paper Partial evaluation via code generation for static stochastic reaction network models.TillKösterVerfasserInautUniversity of Rostock, Institute for Visual and Analytic Computinghttp://purl.uni-rostock.de/rosdok/id00002648urn:nbn:de:gbv:28-rosdok_id00002648-910.18453/rosdok_id00002648004 InformatikFakultät für Informatik und ElektrotechnikCC BY-SA 4.0Nutzungsrechte erteiltLizenz Metadaten: CC0frei zugänglich (Open Access)en2020University of RostockRostockmonographic20202020University Library of RostockRostock2020Universitätsbibliothek Rostockhttp://purl.uni-rostock.de/rosdok/id00002648[{"name":"Köster, Till","affil":"University of Rostock, Institute for Visual and Analytic Computing"}]Till KösterTill Köster. Partial evaluation via code generation for static stochastic reaction network models.In: ACM SIGSIM Conference on Principles of Advanced Discrete Simulation (PADS 2020), June 15-17, 2020, Miami, Florida, USA.Forschungsdaten zuKöster, TillUniversity of Rostock, Institute for Visual and Analytic Computingdoctype:epubdoctype:epubDokumenttypDocument typedoctype:datadoctype:dataDatenpublikationdata publicationdiniPublType:ResearchData diniPublType2022:ResearchDatainfo:eu-repo/semantics/otherdocumentnatureOfContent:ppn_857755366natureOfContent:ppn_857755366ForschungsdatendiniPublType2022:ResearchDatadiniPublType2022:ResearchDataForschungsdatenResearch dataKDSFSDNB:004SDNB:004004 Informatik004 Data processing Computer sciencesinstitution:unirostockinstitution:unirostockUniversität RostockUniversity of RostockUniversität RostockUniversität RostockUni.Rostockhttp://d-nb.info/gnd/38329-6institution:unirostock.iefinstitution:unirostock.iefFakultät für Informatik und ElektrotechnikFaculty of Computer Science and Electrical EngineeringUniversität Rostock. Fakultät für Informatik und ElektrotechnikFakultät für Informatik<br />und ElektrotechnikUni.Rostock.Fakultaet.IEFhttp://d-nb.info/gnd/10085032-7licenseinfo:worklicenseinfo:workWerkworklicenseinfo:work.cclicenselicenseinfo:work.cclicenseCC-LizenzCC-licenselicenseinfo:work.cclicense.cc-by-salicenseinfo:work.cclicense.cc-by-saCC BY-SACC BY-SAlicenseinfo:work.cclicense.cc-by-sa.v40licenseinfo:work.cclicense.cc-by-sa.v40CC BY-SA 4.0CC BY-SA 4.0/creativecommons/l/by-sa/4.0/88x31.png[DE-28]Namensnennung - Weitergabe unter gleichen Bedingungen 4.0 International$cCC BY-SA 4.0$gCreative Commons$uhttps://creativecommons.org/licenses/by-sa/4.0/https://creativecommons.org/licenses/by-sa/4.0/https://creativecommons.org/licenses/by-sa/4.0/licenseinfo:depositlicenseinfo:depositVeröffentlichungsgenehmigungpermission to storelicenseinfo:deposit.rightsgrantedlicenseinfo:deposit.rightsgrantedNutzungsrechte erteiltrights grantedlicenseinfo:metadatalicenseinfo:metadataLizenzen für Metadatenlicenseinfo:metadata.cc0licenseinfo:metadata.cc0Lizenz Metadaten: CC0license metadata: CC0/creativecommons/p/zero/1.0/88x31.pnghttps://creativecommons.org/publicdomain/zero/1.0/accesscondition:openaccessaccesscondition:openaccessfrei zugänglich (Open Access)open accesshttp://purl.org/coar/access_right/c_abf2OAfreeinfo:eu-repo/semantics/openAccess[DE-28]Open Access$gControlled Vocabulary for Access Rights$uhttp://purl.org/coar/access_right/c_abf2rfc5646:enrfc5646:enEnglischEnglishengengPartial evaluation via code generation for static stochastic reaction network models (Software Appendix)[research data]Partial evaluation via code generation for static stochastic reaction network models (Software Appendix)[research data]rosdok_document_0000016057-d495742e65Till KösterTill KösterTill KösterTill KösterTill KösterRostockUniversity of Rostockdatahttp://purl.uni-rostock.de/rosdok/id00002648urn:nbn:de:gbv:28-rosdok_id00002648-910.18453/rosdok_id00002648Succinct, declarative, and domain-specific modeling languages have many advantages when creating simulation models. However, it is often challenging to efficiently execute models defined in such languages. We use code generation for model-specific simulators. Code generation has been successfully applied for high-performance algorithms in many application domains. By generating tailored simulators for specific simulation models defined in a domain specific language, we get the best of both worlds: a succinct, declarative and formal presentation of the model and an efficient execution. We illustrate this based on a simple domain-specific language for biochemical reaction networks as well as on the network representation of the established BioNetGen language. We implement two approaches adopting the same simulation algorithms: one generic simulator that parses models at runtime and one generator that produces a simulator specialized to a given model based on partial evaluation and code generation. Akin to profile-guided optimization we also use dynamic execution of the model to further optimize the simulators. The performance of the approaches is carefully benchmarked using representative models of small to mid-sized biochemical reaction networks. The generic simulator achieves a performance similar to state of the art simulators in the domain, whereas the specialized simulator outperforms established simulation algorithms with a speedup of more than an order of magnitude. This repository contains the code generation software as described in the 2020 PADS paper Partial evaluation via code generation for static stochastic reaction network models.20202020Till Köster. Partial evaluation via code generation for static stochastic reaction network models.In: ACM SIGSIM Conference on Principles of Advanced Discrete Simulation (PADS 2020), June 15-17, 2020, Miami, Florida, USA.Till Köster. Partial evaluation via code generation for static stochastic reaction network models.In: ACM SIGSIM Conference on Principles of Advanced Discrete Simulation (PADS 2020), June 15-17, 2020, Miami, Florida, USA.Forschungsdaten zu[{"name":"Köster, Till","affil":"University of Rostock, Institute for Visual and Analytic Computing"}]Till Kösterdata1 Koester_2020_data.zip 282940 cc7b961a6a9559c3f0e5e1c80d401aea 2 Koester_2020_documentation.pdf 48268 0beaa24d33962cd5a52523b2f9014abc 1697803288Oau2020-05-112023-08-05T16:41:46ZrdaConverted from PICA to MODS using Pica2Mods XSLT Transformer 2.7 [SCM: "0c0e7a3c226a4a0cbcbec39b493c3c5257339ab8" "v2.7" "2023-08-04T00:00:00+0200"] with mode 'DEFAULT'.rosdok/id00002648DatenpublikationForschungsdatenPartial evaluation via code generation for static stochastic reaction network models (Software Appendix)[research data]Succinct, declarative, and domain-specific modeling languages have many advantages when creating simulation models. However, it is often challenging to efficiently execute models defined in such languages. We use code generation for model-specific simulators. Code generation has been successfully applied for high-performance algorithms in many application domains. By generating tailored simulators for specific simulation models defined in a domain specific language, we get the best of both worlds: a succinct, declarative and formal presentation of the model and an efficient execution. We illustrate this based on a simple domain-specific language for biochemical reaction networks as well as on the network representation of the established BioNetGen language. We implement two approaches adopting the same simulation algorithms: one generic simulator that parses models at runtime and one generator that produces a simulator specialized to a given model based on partial evaluation and code generation. Akin to profile-guided optimization we also use dynamic execution of the model to further optimize the simulators. The performance of the approaches is carefully benchmarked using representative models of small to mid-sized biochemical reaction networks. The generic simulator achieves a performance similar to state of the art simulators in the domain, whereas the specialized simulator outperforms established simulation algorithms with a speedup of more than an order of magnitude. This repository contains the code generation software as described in the 2020 PADS paper Partial evaluation via code generation for static stochastic reaction network models.TillKösterVerfasserInautUniversity of Rostock, Institute for Visual and Analytic Computinghttp://purl.uni-rostock.de/rosdok/id00002648urn:nbn:de:gbv:28-rosdok_id00002648-910.18453/rosdok_id00002648004 InformatikFakultät für Informatik und ElektrotechnikCC BY-SA 4.0Nutzungsrechte erteiltLizenz Metadaten: CC0frei zugänglich (Open Access)en2020University of RostockRostockmonographic20202020University Library of RostockRostock2020Universitätsbibliothek Rostockhttp://purl.uni-rostock.de/rosdok/id00002648[{"name":"Köster, Till","affil":"University of Rostock, Institute for Visual and Analytic Computing"}]Till KösterTill Köster. Partial evaluation via code generation for static stochastic reaction network models.In: ACM SIGSIM Conference on Principles of Advanced Discrete Simulation (PADS 2020), June 15-17, 2020, Miami, Florida, USA.Forschungsdaten zu Köster, Till University of Rostock, Institute for Visual and Analytic Computing 2020-03-27T08:50:06.762Z 2023-08-08T12:04:15.681Z 2023-08-18T12:04:15.685Z editorP {"identifier":"http://purl.uni-rostock.de/rosdok/id00002648","type":"purl","additional":"","service":"RosDokPURL","created":"2021-08-01T16:53:14.540Z","registered":"2021-08-01T16:53:14.540Z"} {"identifier":"10.18453/rosdok_id00002648","type":"doi","additional":"","service":"RosDokDOI","created":"2021-08-01T16:53:14.545Z","registrationStarted":"2021-08-01T16:53:14.543Z","registered":"2021-08-10T17:02:56.086Z"} {"identifier":"rosdok/id00002648","type":"local_id","additional":"","service":"MCRLocalID","created":"2023-08-08T12:04:15.645Z"} administratordatadocumentationPartial evaluation via code generation for static stochastic reaction network models (Software Appendix)[research data]Partial evaluation via code generation for static stochastic reaction network models (Software Appendix)[research data]rosdok_document_0000016057-d495742e65Till KösterTill KösterTill KösterTill KösterTill KösterRostockUniversity of Rostockdatahttp://purl.uni-rostock.de/rosdok/id00002648urn:nbn:de:gbv:28-rosdok_id00002648-910.18453/rosdok_id00002648Succinct, declarative, and domain-specific modeling languages have many advantages when creating simulation models. However, it is often challenging to efficiently execute models defined in such languages. We use code generation for model-specific simulators. Code generation has been successfully applied for high-performance algorithms in many application domains. By generating tailored simulators for specific simulation models defined in a domain specific language, we get the best of both worlds: a succinct, declarative and formal presentation of the model and an efficient execution. We illustrate this based on a simple domain-specific language for biochemical reaction networks as well as on the network representation of the established BioNetGen language. We implement two approaches adopting the same simulation algorithms: one generic simulator that parses models at runtime and one generator that produces a simulator specialized to a given model based on partial evaluation and code generation. Akin to profile-guided optimization we also use dynamic execution of the model to further optimize the simulators. The performance of the approaches is carefully benchmarked using representative models of small to mid-sized biochemical reaction networks. The generic simulator achieves a performance similar to state of the art simulators in the domain, whereas the specialized simulator outperforms established simulation algorithms with a speedup of more than an order of magnitude. This repository contains the code generation software as described in the 2020 PADS paper Partial evaluation via code generation for static stochastic reaction network models.20202020Till Köster. Partial evaluation via code generation for static stochastic reaction network models.In: ACM SIGSIM Conference on Principles of Advanced Discrete Simulation (PADS 2020), June 15-17, 2020, Miami, Florida, USA.Till Köster. Partial evaluation via code generation for static stochastic reaction network models.In: ACM SIGSIM Conference on Principles of Advanced Discrete Simulation (PADS 2020), June 15-17, 2020, Miami, Florida, USA.Forschungsdaten zu[{"name":"Köster, Till","affil":"University of Rostock, Institute for Visual and Analytic Computing"}]Till Köster[xslt]Saxonrosdok/id00002648https://purl.uni-rostock.de/rosdok/id00002648169780328810.18453/rosdok_id00002648urn:nbn:de:gbv:28-rosdok_id00002648-9Till KösterKöster TillPartial evaluation via code generation for static stochastic reaction network models (Software Appendix) : [research data]Datenpublikationdata publicationUniversity of Rostock, 2020Succinct, declarative, and domain-specific modeling languages have many advantages when creating simulation models. However, it is often challenging to efficiently execute models defined in such languages. We use code generation for model-specific simulators. Code generation has been successfully…Till KösterPartial evaluation via code generation for static stochastic reaction network models (Software Appendix)[research data]Till Köster. Partial evaluation via code generation for static stochastic reaction network models.In: ACM SIGSIM Conference on Principles of Advanced Discrete Simulation (PADS 2020), June 15-17, 2020, Miami, Florida, USA.Universitätsbibliothek Rostockhttp://purl.uni-rostock.de/rosdok/id00002648TillKösterVerfasserInautUniversity of Rostock, Institute for Visual and Analytic Computing[purl]http://purl.uni-rostock.de/rosdok/id00002648[urn]urn:nbn:de:gbv:28-rosdok_id00002648-9[doi]10.18453/rosdok_id00002648open_access20202020epoch:21th_centuryrfc5646:endoctype:dataaccesscondition:openaccessSDNB:004institution:unirostock.iefstate:published