Publication: Exploiting stream parallelism of MRI reconstruction using GrPPI over multiple back-ends
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2019-07-04
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IEEE
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Abstract
In recent years, on-line processing of data streams has been established as a major computing paradigm. This is due mainly to two reasons: first, more and more data are generated in near real-time that need to be processed; the second reason is given by the need of efficient parallel applications. However, the above-mentioned areas expose a tough challenge over traditional data-analysis techniques, which have been forced to evolve to a stream perspective. In this work we present an comparative study of a stream-aware multi-staged application, which has been implemented using GrPPI, a generic and reusable parallel pattern interface for C++ applications. We demonstrate the benefits of using this interface in terms of programability, performance, and scalability.
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Proceeding of: 19th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID), Larnaca, Cyprus, 14-17 May 2019
ASPIDE: Exascale programIng models for extreme data processing
ASPIDE: Exascale programIng models for extreme data processing
Keywords
MRI reconstruction, Stream parallelism, Grppi
Bibliographic citation
2019 19th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID), 14-17 May 2019, Larnaca, Cyprus, Pp. 631-637