@inproceedings{Tremarin2024,
title = {Fortran DO CONCURRENT Evaluation in Multi-core for NAS-PB Conjugate Gradient and a Porous Media Application},
author = {Gabriel Dineck Tremarin and Anna Victoria Gonc¸alves Marciano and Claudio Schepke and Adriano Vogel},
editor = {Gabriel Dineck Tremarin and Anna Victoria Gonc¸alves Marciano and Claudio Schepke and Adriano Vogel},
url = {https://larcc.setrem.com.br/wp-content/uploads/2025/03/244796_1.pdf},
year = {2024},
date = {2024-10-23},
journal = {Simpósio em Sistemas Computacionais de Alto Desempenho (SSCAD)},
pages = {133--143},
abstract = {High-performance computing exploits the hardware resources available to accelerate the applications’ executions, whereas achieving such an exploitation of hardware resources demands software programming. Hence, several parallel programming interfaces (PPIs) are used for sequential programs to call thread resources and parallelism routines. There are explicit PPIs (eg, Pthreads and TBB) or implicit (eg, OpenMP and OpenACC). Another approach is parallel programming languages like the Fortran 2008 specification, which natively provides the DO CONCURRENT resource. However, DO CONCURRENT’s evaluation is still limited. In this paper, we explore and compare the native parallelism of FORTRAN with the directives provided by the OpenMP and OpenACC PPIs in the NAS-PB CG benchmark and a porous media application. The results show that the DO CONCURRENT provides parallel CPU code with numerical compatibility for scientific applications. Moreover, DO CONCURRENT achieves in multi-cores a performance comparable to and even slightly better than other PPIs, such as OpenMP. Our work also contributes with a method to use DO CONCURRENT.},
keywords = {multicore, Parallel programming, parallelism},
pubstate = {published},
tppubtype = {inproceedings}
}

@article{Supercomputing,
title = {Enhancing self-adaptation for efficient decision-making at run-time in streaming applications on multicores},
author = {Adriano Vogel and Marco Danelutto and Massimo Torquati and Dalvan Griebler and Luiz Gustavo Fernandes },
editor = {Adriano Vogel and Marco Danelutto and Massimo Torquati and Dalvan Griebler and Luiz Gustavo Fernandes },
url = { https://link.springer.com/article/10.1007/s11227-024-06191-w},
doi = {10.1007/s11227-024-06191-w},
year = {2024},
date = {2024-06-21},
journal = {The Journal of Supercomputing},
pages = {1573-0484},
abstract = {Parallel computing is very important to accelerate the performance of computing applications. Moreover, parallel applications are expected to continue executing in more dynamic environments and react to changing conditions. In this context, applying self-adaptation is a potential solution to achieve a higher level of autonomic abstractions and runtime responsiveness. In our research, we aim to explore and assess the possible abstractions attainable through the transparent management of parallel executions by self-adaptation. Our primary objectives are to expand the adaptation space to better reflect real-world applications and assess the potential for self-adaptation to enhance efficiency. We provide the following scientific contributions: (I) A conceptual framework to improve the designing of self-adaptation; (II) A new decision-making strategy for applications with multiple parallel stages; (III) A comprehensive evaluation of the proposed decision-making strategy compared to the state-of-the-art. The results demonstrate that the proposed conceptual framework can help design and implement self-adaptive strategies that are more modular and reusable. The proposed decision-making strategy provides significant gains in accuracy compared to the state-of-the-art, increasing the parallel applications’ performance and efficiency.},
keywords = {multicore, Parallel computing, Stream processing},
pubstate = {published},
tppubtype = {article}
}