SkePU 3: Portable High-Level Programming of Heterogeneous Systems and HPC Clusters

We present the third generation of the C++-based open-source skeleton programming framework SkePU. Its main new features include new skeletons, new data container types, support for returning multiple objects from skeleton instances and user functions, support for specifying alternative platform-spe...

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Vydané v:International journal of parallel programming Ročník 49; číslo 6; s. 846 - 866
Hlavní autori: Ernstsson, August, Ahlqvist, Johan, Zouzoula, Stavroula, Kessler, Christoph
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York Springer US 01.12.2021
Springer Nature B.V
Predmet:
2020
Automation
C plus plus
Clusters
Co-design
Computer Science
Containers
Data retrieval
High performance computing
Information sharing
Interfaces
Performance evaluation
Processor Architectures
Software
Software Engineering/Programming and Operating Systems
Special Issue on High-Level Parallel Programming and Applications
Theory of Computation
Co-design approach
Heterogeneous computing
Skeleton programming
High-level parallel programming
Cluster computing
ISSN:0885-7458, 1573-7640, 1573-7640
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Popis
Shrnutí:We present the third generation of the C++-based open-source skeleton programming framework SkePU. Its main new features include new skeletons, new data container types, support for returning multiple objects from skeleton instances and user functions, support for specifying alternative platform-specific user functions to exploit e.g. custom SIMD instructions, generalized scheduling variants for the multicore CPU backends, and a new cluster-backend targeting the custom MPI interface provided by the StarPU task-based runtime system. We have also revised the smart data containers’ memory consistency model for automatic data sharing between main and device memory. The new features are the result of a two-year co-design effort collecting feedback from HPC application partners in the EU H2020 project EXA2PRO, and target especially the HPC application domain and HPC platforms. We evaluate the performance effects of the new features on high-end multicore CPU and GPU systems and on HPC clusters.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:0885-7458
1573-7640
1573-7640
DOI:10.1007/s10766-021-00704-3