Dalvan Griebler

@article{MENCAGLI:JPDC:24,

title = {General-purpose data stream processing on heterogeneous architectures with WindFlow},

author = {Gabriele Mencagli and Massimo Torquati and Dalvan Griebler and Alessandra Fais and Marco Danelutto},

url = {https://www.sciencedirect.com/science/article/pii/S0743731523001521},

doi = {https://doi.org/10.1016/j.jpdc.2023.104782},

year  = {2024},

date = {2024-02-01},

journal = {Journal of Parallel and Distributed Computing},

volume = {184},

pages = {104782},

publisher = {Elsevier},

abstract = {Many emerging applications analyze data streams by running graphs of communicating tasks called operators. To develop and deploy such applications, Stream Processing Systems (SPSs) like Apache Storm and Flink have been made available to researchers and practitioners. They exhibit imperative or declarative programming interfaces to develop operators running arbitrary algorithms working on structured or unstructured data streams. In this context, the interest in leveraging hardware acceleration with GPUs has become more pronounced in high-throughput use cases. Unfortunately, GPU acceleration has been studied for relational operators working on structured streams only, while non-relational operators have often been overlooked. This paper presents WindFlow, a library supporting the seamless GPU offloading of general partitioned-stateful operators, extending the range of operators that benefit from hardware acceleration. Its design provides high throughput still exposing a high-level API to users compared with the raw utilization of GPUs in Apache Flink.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@inproceedings{BIANCHESSI:WSCAD:23,

title = {Conversão do NAS Parallel Benchmarks para C++ Standard},

author = {Arthur S. Bianchessi and Leonardo Mallmann and Renato Barreto Hoffmann and Dalvan Griebler},

url = {https://doi.org/},

year  = {2023},

date = {2023-10-01},

booktitle = {Anais do XXIII Simpósio em Sistemas Computacionais de Alto Desempenho (WSCAD)},

pages = {1-12},

publisher = {SBC},

address = {Porto Alegre, Brasil},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{ALF:WSCAD:23,

title = {Análise da Execução de Algoritmos de Aprendizado de Máquina em Dispositivos Embarcados},

author = {Lucas Alf and Renato Barreto Hoffmann and Caetano Müller and Dalvan Griebler},

url = {https://doi.org/},

year  = {2023},

date = {2023-10-01},

booktitle = {Anais do XXIII Simpósio em Sistemas Computacionais de Alto Desempenho (WSCAD)},

pages = {1-12},

publisher = {SBC},

address = {Porto Alegre, Brasil},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{ANDRADE:WSCAD:23,

title = {Extending the Planning Poker Method to Estimate the Development Effort of Parallel Applications},

author = {Gabriella Andrade and Dalvan Griebler and Rodrigo Santos and Luiz Gustavo Fernandes},

url = {https://doi.org/},

year  = {2023},

date = {2023-10-01},

booktitle = {Anais do XXIII Simpósio em Sistemas Computacionais de Alto Desempenho (WSCAD)},

pages = {1-12},

publisher = {SBC},

address = {Porto Alegre, Brasil},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{larcc:MALISZEWSKI:SBAC-PADW:23,

title = {Evaluation Model and Performance Analysis of NIC Aggregations in Containerized Private Clouds},

author = {Anderson Matthias Maliszewski and Dalvan Griebler and Eduardo Roloff and Rodrigo Rosa Righi and Philippe O. A. Navaux},

url = {https://doi.org/10.1109/SBAC-PADW60351.2023.00025},

doi = {10.1109/SBAC-PADW60351.2023.00025},

year  = {2023},

date = {2023-10-01},

booktitle = {International Symposium on Computer Architecture and High Performance Computing Workshops (SBAC-PADW)},

pages = {101-107},

publisher = {IEEE},

address = {Porto Alegre, Brazil},

series = {SBAC-PADW'23},

abstract = {The availability of computational resources changed significantly due to cloud computing. In addition, we have witnessed efforts to execute High-Performance Computing (HPC) applications in the cloud attracted by the advantages of cost savings and scalable/elastic resource allocation. Allocating more powerful hardware and exclusivity allocating resources such as memory, storage, and CPU can improve performance in the cloud. For network interconnection, significant noise, and other inferences are generated by several simultaneous instances (multitenants) communicating using the same network. As increasing the network bandwidth may be an alternative, we designed an evaluation model, and performance analysis of NIC aggregation approaches in containerized private clouds. The experiments using NAS Parallel Benchmarks revealed that NIC aggregation approach outperforms the baseline up to ≈98% of the executions with applications characterized by intensive network use. Also, the Balance Round-Robin aggregation mode performed better than the 802.3ad aggregation mode in most assessments.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}