Providing high‐level self‐adaptive abstractions for stream parallelism on multicores

Author:

Vogel Adriano1ORCID,Griebler Dalvan12ORCID,Fernandes Luiz Gustavo1ORCID

Affiliation:

1. School of Technology Pontifical Catholic University of Rio Grande do Sul Rio Grande do Sul Brazil

2. Laboratory of Advanced Research on Cloud Computing Sociedade Educacional Três de Maio (SETREM) Rio Grande do Sul Brazil

Funder

Conselho Nacional de Desenvolvimento Científico e Tecnológico

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul

Publisher

Wiley

Subject

Software

Cited by 9 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Enhancing self-adaptation for efficient decision-making at run-time in streaming applications on multicores;The Journal of Supercomputing;2024-06-21

2. An Efficient Task-Parallel Pipeline Programming Framework;Proceedings of the International Conference on High Performance Computing in Asia-Pacific Region;2024-01-18

3. A systematic mapping of performance in distributed stream processing systems;2023 49th Euromicro Conference on Software Engineering and Advanced Applications (SEAA);2023-09-06

4. Revisiting self-adaptation for efficient decision-making at run-time in parallel executions;2023 31st Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP);2023-03

5. Efficient. Scalable and Robust Data Shuffle Service for Distributed MapReduce Computing on Cloud;2022 IEEE 24th Int Conf on High Performance Computing & Communications; 8th Int Conf on Data Science & Systems; 20th Int Conf on Smart City; 8th Int Conf on Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys);2022-12

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