Stream parallelism with ordered data constraints on multi-core systems
| Autor: | Renato B. Hoffmann, Marco Danelutto, Dalvan Griebler, Luiz Gustavo Fernandes |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
0301 basic medicine
Multi-core processor Parallel data compression Parallel programming Parallel stream processing Parallel video streaming Software Theoretical Computer Science Information Systems Hardware and Architecture Computer science Data stream mining Computation 02 engineering and technology Parallel computing 020202 computer hardware & architecture 03 medical and health sciences 030104 developmental biology 0202 electrical engineering electronic engineering information engineering Data compression |
| Popis: | It is often a challenge to keep input/output tasks/results in order for parallel computations over data streams, particularly when stateless task operators are replicated to increase parallelism when there are irregular tasks. Maintaining input/output order requires additional coding effort and may significantly impact the application’s actual throughput. Thus, we propose a new implementation technique designed to be easily integrated with any of the existing C++ parallel programming frameworks that support stream parallelism. In this paper, it is first implemented and studied using SPar, our high-level domain-specific language for stream parallelism. We discuss the results of a set of experiments with real-world applications revealing how significant performance improvements may be achieved when our proposed solution is integrated within SPar, especially for data compression applications. Also, we show the results of experiments performed after integrating our solution within FastFlow and TBB, revealing no significant overheads. |
| Databáze: | OpenAIRE |
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