Automatic problem size sensitive task partitioning on heterogeneous parallel systems
| dc.contributor.author | Grasso, Ivan | |
| dc.contributor.author | Kofler, Klaus | |
| dc.contributor.author | Cosenza, Biagio | |
| dc.contributor.author | Fahringer, Thomas | |
| dc.date.accessioned | 2017-10-24T10:05:26Z | |
| dc.date.available | 2017-10-24T10:05:26Z | |
| dc.date.issued | 2013 | |
| dc.description.abstract | In this paper we propose a novel approach which automatizes task partitioning in heterogeneous systems. Our framework is based on the Insieme Compiler and Runtime infrastructure. The compiler translates a single-device OpenCL program into a multi-device OpenCL program. The runtime system then performs dynamic task partitioning based on an offline-generated prediction model. In order to derive the prediction model, we use a machine learning approach that incorporates static program features as well as dynamic, input sensitive features. Our approach has been evaluated over a suite of 23 programs and achieves performance improvements compared to an execution of the benchmarks on a single CPU and a single GPU only. | en |
| dc.identifier.isbn | 978-1-4503-1922-5 | |
| dc.identifier.issn | 0362-1340 | |
| dc.identifier.uri | https://depositonce.tu-berlin.de/handle/11303/6927 | |
| dc.identifier.uri | http://dx.doi.org/10.14279/depositonce-6266 | |
| dc.language.iso | en | |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | |
| dc.subject.ddc | 004 Datenverarbeitung; Informatik | |
| dc.subject.other | code analysis | en |
| dc.subject.other | compilers | en |
| dc.subject.other | gpu | en |
| dc.subject.other | heterogeneous computing | en |
| dc.subject.other | machine learning | en |
| dc.subject.other | runtime system | en |
| dc.subject.other | task partitioning | en |
| dc.title | Automatic problem size sensitive task partitioning on heterogeneous parallel systems | en |
| dc.type | Article | en |
| dc.type.version | acceptedVersion | en |
| dcterms.bibliographicCitation.doi | 10.1145/2442516.2442545 | |
| dcterms.bibliographicCitation.doi | 10.1145/2517327.2442545 | |
| dcterms.bibliographicCitation.issue | 8 | |
| dcterms.bibliographicCitation.journaltitle | ACM SIGPLAN Notices | en |
| dcterms.bibliographicCitation.originalpublishername | Association for Computing Machinery (ACM) | en |
| dcterms.bibliographicCitation.originalpublisherplace | New York, NY | en |
| dcterms.bibliographicCitation.pageend | 282 | |
| dcterms.bibliographicCitation.pagestart | 281 | |
| dcterms.bibliographicCitation.volume | 48 | |
| tub.accessrights.dnb | domain | |
| tub.affiliation | Fak. 4 Elektrotechnik und Informatik::Inst. Technische Informatik und Mikroelektronik::FG Architektur eingebetteter Systeme | de |
| tub.affiliation.faculty | Fak. 4 Elektrotechnik und Informatik | de |
| tub.affiliation.group | FG Architektur eingebetteter Systeme | de |
| tub.affiliation.institute | Inst. Technische Informatik und Mikroelektronik | de |
| tub.publisher.universityorinstitution | Technische Universität Berlin | en |
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