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dc.contributor.authorHelal, A.en
dc.contributor.authorSathre, P.en
dc.contributor.authorFeng, W.en
dc.coverage.spatialSalt Lake City, Utah, USAen
dc.date.accessioned2017-04-03T04:54:36Zen
dc.date.available2017-04-03T04:54:36Zen
dc.date.issued2016-11-15en
dc.identifier.isbn978-1-4673-8815-3en
dc.identifier.issn2167-4337en
dc.identifier.urihttp://hdl.handle.net/10919/76745en
dc.description.abstractTo attain scalable performance efficiently, the HPC community expects future exascale systems to consist of multiple nodes, each with different types of hardware accelerators. In addition to GPUs and Intel MICs, additional candidate accelerators include embedded multiprocessors and FPGAs. End users need appropriate tools to efficiently use the available compute resources in such systems, both within a compute node and across compute nodes. As such, we present MetaMorph, a library framework designed to (automatically) extract as much computational capability as possible from HPC systems. Its design centers around three core principles: abstraction, interoperability, and adaptivity. To demonstrate its efficacy, we present a case study that uses the structured grids design pattern, which is heavily used in computational fluid dynamics. We show how MetaMorph significantly reduces the development time, while delivering performance and interoperability across an array of heterogeneous devices, including multicore CPUs, Intel MICs, AMD GPUs, and NVIDIA GPUs.en
dc.format.extent119 - 129 (11) page(s)en
dc.relation.ispartofACM/IEEE SC16: The International Conference for High Performance Computing, Networking, Storage and Analysis (also known as Supercomputing)en
dc.relation.urihttp://www.cs.vt.edu/~fengen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectLibrariesen
dc.subjectHardwareen
dc.subjectInteroperabilityen
dc.subjectKernelen
dc.subjectPerformance Evaluationen
dc.subjectExascaleen
dc.subjectParallel Librariesen
dc.subjectPerformance Portabilityen
dc.subjectProgrammabilityen
dc.subjectAcceleratorsen
dc.subjectGPUen
dc.subjectMICen
dc.subjectCUDAen
dc.subjectOpenCLen
dc.subjectOpenMPen
dc.subjectMPIen
dc.subjectStructured Gridsen
dc.titleMetaMorph: A Library Framework for Interoperable Kernels on Multi- and Many-Core Clustersen
dc.typeConference proceedingen
dc.description.versionPublished (Publication status)en
dc.contributor.departmentElectrical and Computer Engineeringen
dc.contributor.departmentComputer Scienceen
dc.description.notesYes, full paper (Peer reviewed?)en
dc.title.serialACM/IEEE SC16: The International Conference for High Performance Computing, Networking, Storage and Analysis (also known as Supercomputing)en
dc.identifier.doihttps://doi.org/10.1109/SC.2016.10en
pubs.finish-date2016-11-18en
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.organisational-group/Virginia Tech/Engineeringen
pubs.organisational-group/Virginia Tech/Engineering/COE T&R Facultyen
pubs.organisational-group/Virginia Tech/Engineering/Computer Scienceen
pubs.organisational-group/Virginia Tech/Faculty of Health Sciencesen
pubs.start-date2016-11-13en


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