Leveraging Hybrid Cloud HPC with Multitier Reactive Programming
The advent of cloud computing has enabled large-scale availability of on-demand computing and storage resources. However, these benefits are not yet at the fingertips of HPC developers: Typical HPC applications use on-premise computing resources and rely on static deployment setups, reliable hardwar...
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| Published in: | 2020 IEEE/ACM International Workshop on Interoperability of Supercomputing and Cloud Technologies (SuperCompCloud) pp. 27 - 32 |
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| Main Authors: | , , , |
| Format: | Conference Proceeding |
| Language: | English |
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IEEE
01.11.2020
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| Abstract | The advent of cloud computing has enabled large-scale availability of on-demand computing and storage resources. However, these benefits are not yet at the fingertips of HPC developers: Typical HPC applications use on-premise computing resources and rely on static deployment setups, reliable hardware, and rather homogeneous resources. This hinders (partial) execution in the cloud, even though applications could benefit from scaling beyond on-premise resources and from the variety of hardware available in the cloud to speed up execution. To address this issue, we orchestrate computationally intensive kernels using a high-level programming language that ensures advanced optimization and improves execution flexibility-enabling hybrid cloud/on-premise HPC deployments. Our approach is based on multitier reactive programming, where distributed code is defined within the same compilation unit and computations are placed explicitly using placement types. We adjust placement based on performance characteristics measured before execution, apply our approach to a shortest vector problem (SVP) solver from cryptanalysis, and evaluate it to be effective. |
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| AbstractList | The advent of cloud computing has enabled large-scale availability of on-demand computing and storage resources. However, these benefits are not yet at the fingertips of HPC developers: Typical HPC applications use on-premise computing resources and rely on static deployment setups, reliable hardware, and rather homogeneous resources. This hinders (partial) execution in the cloud, even though applications could benefit from scaling beyond on-premise resources and from the variety of hardware available in the cloud to speed up execution. To address this issue, we orchestrate computationally intensive kernels using a high-level programming language that ensures advanced optimization and improves execution flexibility-enabling hybrid cloud/on-premise HPC deployments. Our approach is based on multitier reactive programming, where distributed code is defined within the same compilation unit and computations are placed explicitly using placement types. We adjust placement based on performance characteristics measured before execution, apply our approach to a shortest vector problem (SVP) solver from cryptanalysis, and evaluate it to be effective. |
| Author | Sokolowski, Daniel Lehr, Jan-Patrick Salvaneschi, Guido Bischof, Christian |
| Author_xml | – sequence: 1 givenname: Daniel surname: Sokolowski fullname: Sokolowski, Daniel email: sokolowski@cstu-darmstadt.de organization: Technical University of Darmstadt,Reactive Programming Technology,Germany – sequence: 2 givenname: Jan-Patrick surname: Lehr fullname: Lehr, Jan-Patrick email: jan-patrick.lehr@tu-darmstadt.de organization: Technical University of Darmstadt,Scientific Computing,Germany – sequence: 3 givenname: Christian surname: Bischof fullname: Bischof, Christian email: christian.bischof@tu-darmstadt.de organization: Technical University of Darmstadt,Scientific Computing,Germany – sequence: 4 givenname: Guido surname: Salvaneschi fullname: Salvaneschi, Guido email: guido.salvaneschi@unisg.ch organization: University of St. Gallen,Programming,Switzerland |
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| Snippet | The advent of cloud computing has enabled large-scale availability of on-demand computing and storage resources. However, these benefits are not yet at the... |
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| StartPage | 27 |
| SubjectTerms | cloud computing distributed code Fault tolerance Fault tolerant systems high level languages high level programming language High Performance Computing Hybrid Cloud Computing hybrid cloud HPC Kernel Materials requirements planning Multitier Programming multitier reactive programming on demand computing parallel processing Programming Reactive Programming resource allocation storage resources Topology |
| Title | Leveraging Hybrid Cloud HPC with Multitier Reactive Programming |
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