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2022 |
Löff, Júnior; Hoffmann, Renato Barreto; Pieper, Ricardo; Griebler, Dalvan; Fernandes, Luiz Gustavo DSParLib: A C++ Template Library for Distributed Stream Parallelism Journal Article doi International Journal of Parallel Programming, 50 (5), pp. 454-485, 2022. Abstract | Links | BibTeX | Tags: Distributed computing, Parallel programming @article{LOFF:IJPP:22, title = {DSParLib: A C++ Template Library for Distributed Stream Parallelism}, author = {Júnior Löff and Renato Barreto Hoffmann and Ricardo Pieper and Dalvan Griebler and Luiz Gustavo Fernandes}, url = {https://doi.org/10.1007/s10766-022-00737-2}, doi = {10.1007/s10766-022-00737-2}, year = {2022}, date = {2022-01-01}, journal = {International Journal of Parallel Programming}, volume = {50}, number = {5}, pages = {454-485}, publisher = {Springer}, abstract = {Stream processing applications deal with millions of data items continuously generated over time. Often, they must be processed in real-time and scale performance, which requires the use of distributed parallel computing resources. In C/C++, the current state-of-the-art for distributed architectures and High-Performance Computing is Message Passing Interface (MPI). However, exploiting stream parallelism using MPI is complex and error-prone because it exposes many low-level details to the programmer. In this work, we introduce a new parallel programming abstraction for implementing distributed stream parallelism named DSParLib. Our abstraction of MPI simplifies parallel programming by providing a pattern-based and building block-oriented development to inter-connect, model, and parallelize data streams found in modern applications. Experiments conducted with five different stream processing applications and the representative PARSEC Ferret benchmark revealed that DSParLib is efficient and flexible. Also, DSParLib achieved similar or better performance, required less coding, and provided simpler abstractions to express parallelism with respect to handwritten MPI programs.}, keywords = {Distributed computing, Parallel programming}, pubstate = {published}, tppubtype = {article} } Stream processing applications deal with millions of data items continuously generated over time. Often, they must be processed in real-time and scale performance, which requires the use of distributed parallel computing resources. In C/C++, the current state-of-the-art for distributed architectures and High-Performance Computing is Message Passing Interface (MPI). However, exploiting stream parallelism using MPI is complex and error-prone because it exposes many low-level details to the programmer. In this work, we introduce a new parallel programming abstraction for implementing distributed stream parallelism named DSParLib. Our abstraction of MPI simplifies parallel programming by providing a pattern-based and building block-oriented development to inter-connect, model, and parallelize data streams found in modern applications. Experiments conducted with five different stream processing applications and the representative PARSEC Ferret benchmark revealed that DSParLib is efficient and flexible. Also, DSParLib achieved similar or better performance, required less coding, and provided simpler abstractions to express parallelism with respect to handwritten MPI programs. |
2021 |
Vanzan, Anthony; Fim, Gabriel; Welter, Greice; Griebler, Dalvan Aceleração da Classificação de Lavouras de Milho com MPI e Estratégias de Paralelismo Inproceedings doi 21th Escola Regional de Alto Desempenho da Região Sul (ERAD-RS), pp. 49-52, Sociedade Brasileira de Computação, Joinville, RS, Brazil, 2021. Abstract | Links | BibTeX | Tags: Agriculture, Distributed computing, Parallel programming, Stream processing @inproceedings{larcc:DL_Classificaiton_MPI:ERAD:21, title = {Aceleração da Classificação de Lavouras de Milho com MPI e Estratégias de Paralelismo}, author = {Anthony Vanzan and Gabriel Fim and Greice Welter and Dalvan Griebler}, url = {https://doi.org/10.5753/eradrs.2021.14772}, doi = {10.5753/eradrs.2021.14772}, year = {2021}, date = {2021-04-01}, booktitle = {21th Escola Regional de Alto Desempenho da Região Sul (ERAD-RS)}, pages = {49-52}, publisher = {Sociedade Brasileira de Computação}, address = {Joinville, RS, Brazil}, abstract = {Este trabalho visou acelerar a execução de um algoritmo de classificação de lavouras em imagens áreas. Para isso, foram implementadas diferentes versões paralelas usando a biblioteca MPI na linguagem Python. A avaliação foi conduzida em dois ambientes computacionais. Conclui-se que é possível reduzir o tempo de execução a medida que mais recursos paralelos são usados e a estratégia de distribuição de trabalho dinâmica é mais eficiente.}, keywords = {Agriculture, Distributed computing, Parallel programming, Stream processing}, pubstate = {published}, tppubtype = {inproceedings} } Este trabalho visou acelerar a execução de um algoritmo de classificação de lavouras em imagens áreas. Para isso, foram implementadas diferentes versões paralelas usando a biblioteca MPI na linguagem Python. A avaliação foi conduzida em dois ambientes computacionais. Conclui-se que é possível reduzir o tempo de execução a medida que mais recursos paralelos são usados e a estratégia de distribuição de trabalho dinâmica é mais eficiente. |