Supporting MPI-distributed stream parallel patterns in GrPPI
| Autor: | David del Rio Astorga, Javier Prieto Cepeda, Manuel F. Dolz, J. Daniel Garcia, Javier Muñoz |
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| Rok vydání: | 2018 |
| Předmět: |
Flexibility (engineering)
Generic programming Computer science Distributed computing Pipeline (computing) 020207 software engineering 02 engineering and technology Stream processing Mode (computer interface) 020204 information systems 0202 electrical engineering electronic engineering information engineering Scalable algorithms Stream data Generic interface |
| Zdroj: | EuroMPI |
| DOI: | 10.1145/3236367.3236380 |
| Popis: | In the recent years, the large volumes of stream data and the near real-time requirements of data streaming applications have exacerbated the need for new scalable algorithms and programming interfaces for distributed and shared-memory platforms. To contribute in this direction, this paper presents a new distributed MPI back end for GrPPI, a C++ high-level generic interface of data-intensive and stream processing parallel patterns. This back end, as a new execution policy, supports the distributed and hybrid (distributed and shared-memory) parallel execution of the Pipeline and Farm patterns, where the hybrid mode combines the MPI policy with a GrPPI shared-memory one. A detailed analysis of the GrPPI MPI execution policy reports considerable benefits from the programmability, flexibility and readability points of view. The experimental evaluation on a streaming application with different distributed and shared-memory scenarios reports considerable performance gains with respect to the sequential versions at the expense of negligible GrPPI overheads. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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