@inproceedings{HOFFMANN:ERAD:21,
title = {Abstraindo o OpenMP no Desenvolvimento de Aplicações de Fluxo de Dados Contínuo},
author = {Renato Barreto Hoffmann and Dalvan Griebler and Luiz Gustavo Fernandes},
url = {https://doi.org/10.5753/eradrs.2021.14777},
doi = {10.5753/eradrs.2021.14777},
year = {2021},
date = {2021-04-01},
booktitle = {21th Escola Regional de Alto Desempenho da Região Sul (ERAD-RS)},
pages = {69-72},
publisher = {Sociedade Brasileira de Computação},
address = {Joinville, Brazil},
abstract = {OpenMP é complexo quando usado para desenvolver aplicações de fluxo de dados. Com o objetivo de mitigar essa dificuldade, foi utilizada uma metodologia existente, chamada SPar, para aumentar o nível de abstração. Portanto, foram utilizadas anotações mais alto-nível da SPar para gerar código mais baixo-nível de fluxo de dados com OpenMP. Os experimentos revelaram que a SPar teve desempenho 0,86% inferior no caso mais extremo.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}

@inproceedings{GARCIA:PDP:21,
title = {Introducing a Stream Processing Framework for Assessing Parallel Programming Interfaces},
author = {Adriano Marques Garcia and Dalvan Griebler and Claudio Schepke and Luiz Gustavo Fernandes},
url = {https://doi.org/10.1109/PDP52278.2021.00021},
doi = {10.1109/PDP52278.2021.00021},
year = {2021},
date = {2021-03-01},
booktitle = {29th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP)},
pages = {84-88},
publisher = {IEEE},
address = {Valladolid, Spain},
series = {PDP'21},
abstract = {Stream Processing applications are spread across different sectors of industry and people's daily lives. The increasing data we produce, such as audio, video, image, and text are demanding quickly and efficiently computation. It can be done through Stream Parallelism, which is still a challenging task and most reserved for experts. We introduce a Stream Processing framework for assessing Parallel Programming Interfaces (PPIs). Our framework targets multi-core architectures and C++ stream processing applications, providing an API that abstracts the details of the stream operators of these applications. Therefore, users can easily identify all the basic operators and implement parallelism through different PPIs. In this paper, we present the proposed framework, implement three applications using its API, and show how it works, by using it to parallelize and evaluate the applications with the PPIs Intel TBB, FastFlow, and SPar. The performance results were consistent with the literature.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}

@inproceedings{VOGEL:PDP:21,
title = {Towards On-the-fly Self-Adaptation of Stream Parallel Patterns},
author = {Adriano Vogel and Gabriele Mencagli and Dalvan Griebler and Marco Danelutto and Luiz Gustavo Fernandes},
url = {https://doi.org/10.1109/PDP52278.2021.00022},
doi = {10.1109/PDP52278.2021.00022},
year = {2021},
date = {2021-03-01},
booktitle = {29th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP)},
pages = {889-93},
publisher = {IEEE},
address = {Valladolid, Spain},
series = {PDP'21},
abstract = {Stream processing applications compute streams of data and provide insightful results in a timely manner, where parallel computing is necessary for accelerating the application executions. Considering that these applications are becoming increasingly dynamic and long-running, a potential solution is to apply dynamic runtime changes. However, it is challenging for humans to continuously monitor and manually self-optimize the executions. In this paper, we propose self-adaptiveness of the parallel patterns used, enabling flexible on-the-fly adaptations. The proposed solution is evaluated with an existing programming framework and running experiments with a synthetic and a real-world application. The results show that the proposed solution is able to dynamically self-adapt to the most suitable parallel pattern configuration and achieve performance competitive with the best static cases. The feasibility of the proposed solution encourages future optimizations and other applicabilities.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}

@article{VOGEL:SPE:21,
title = {Providing High‐Level Self‐Adaptive Abstractions for Stream Parallelism on Multicores},
author = {Adriano Vogel and Dalvan Griebler and Luiz G Fernandes},
url = {https://doi.org/10.1002/spe.2948},
doi = {10.1002/spe.2948},
year = {2021},
date = {2021-01-01},
journal = {Software: Practice and Experience},
volume = {51},
number = {6},
pages = {1194-1217},
publisher = {Wiley Online Library},
abstract = {Stream processing applications are common computing workloads that demand parallelism to increase their performance. As in the past, parallel programming remains a difficult task for application programmers. The complexity increases when application programmers must set non-intuitive parallelism parameters, i.e. the degree of parallelism. The main problem is that state-of-the-art libraries use a static degree of parallelism and are not sufficiently abstracted for developing stream processing applications. In this paper, we propose a self-adaptive regulation of the degree of parallelism to provide higher-level abstractions. Flexibility is provided to programmers with two new self-adaptive strategies, one is for performance experts, and the other abstracts the need to set a performance goal. We evaluated our solution using compiler transformation rules to generate parallel code with the SPar domain-specific language. The experimental results with real-world applications highlighted higher abstraction levels without significant performance degradation in comparison to static executions. The strategy for performance experts achieved slightly higher performance than the one that works without user-defined performance goals.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{BORDIN:IEEEAccess:20,
title = {DSPBench: a Suite of Benchmark Applications for Distributed Data Stream Processing Systems},
author = {Maycon Viana Bordin and Dalvan Griebler and Gabriele Mencagli and Claudio F R Geyer and Luiz Gustavo Fernandes},
url = {https://doi.org/10.1109/ACCESS.2020.3043948},
doi = {10.1109/ACCESS.2020.3043948},
year = {2020},
date = {2020-12-01},
journal = {IEEE Access},
volume = {8},
number = {na},
pages = {222900-222917},
publisher = {IEEE},
abstract = {Systems enabling the continuous processing of large data streams have recently attracted the attention of the scientific community and industrial stakeholders. Data Stream Processing Systems (DSPSs) are complex and powerful frameworks able to ease the development of streaming applications in distributed computing environments like clusters and clouds. Several systems of this kind have been released and currently maintained as open source projects, like Apache Storm and Spark Streaming. Some benchmark applications have often been used by the scientific community to test and evaluate new techniques to improve the performance and usability of DSPSs. However, the existing benchmark suites lack of representative workloads coming from the wide set of application domains that can leverage the benefits offered by the stream processing paradigm in terms of near real-time performance. The goal of this paper is to present a new benchmark suite composed of 15 applications coming from areas like Finance, Telecommunications, Sensor Networks, Social Networks and others. This paper describes in detail the nature of these applications, their full workload characterization in terms of selectivity, processing cost, input size and overall memory occupation. In addition, it exemplifies the usefulness of our benchmark suite to compare real DSPSs by selecting Apache Storm and Spark Streaming for this analysis.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}