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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Veröffentlicht in:2020 IEEE/ACM International Workshop on Interoperability of Supercomputing and Cloud Technologies (SuperCompCloud) S. 27 - 32
Hauptverfasser: Sokolowski, Daniel, Lehr, Jan-Patrick, Bischof, Christian, Salvaneschi, Guido
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.11.2020
Schlagworte:
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
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Zusammenfassung: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.
DOI:10.1109/SuperCompCloud51944.2020.00010