Aplicação da técnica Paramount Iteration nas aplicações BLAST e DNN-ROM na nuvem computacional
Resumo
O crescimento da tendência da computação em nuvem traz novos desafios para a comunidade de computação de alto desempenho. Por possuir um amplo número de recursos, predizer a melhor configuração para uma aplicação especı́fica é uma tarefa custosa e de alto consumo de tempo e principalmente financeiro. A técnica paramount iteration consiste em executar uma parcela da aplicação a fim de determinar o comportamento esperado neste ambiente computacional quando executado por completo. Este artigo valida e utiliza a técnica paramount iteration para as aplicações BLAST e DNN-ROM, sendo possı́vel determinar o melhor ambiente de computação em nuvem para estas.
Referências
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[2] Amazon. Amazon Web Services webpage. https://aws.amazon.com/pt/ec2/.
[3] Amazon. Tipos de instância do Amazon EC2. https://aws.amazon.com/pt/ec2/instance-types/.
[4] R. Aversa, B. Di Martino, M. Rak, S. Venticinque, and U. Villano. Performance prediction for HPC on clouds. In Cloud Computing: Principles and Paradigms, 2011.
[5] A. Bhattacharyya and T. Hoefler. PEMOGEN: Automatic adaptive performance modeling during program runtime. In 23rd International Conference on Parallel Architectures and Compilation, 2014.
[6] R. Escobar and R. V. Boppana. Performance prediction of parallel applications based on small-scale executions. In 23rd International Conference on High Performance Computing, 2016.
[7] H. F. S. Lui and W. Wolf. Construction of reduced order models for fluid flows using deep feedforward neural networks, 2019.
[8] A. Gupta, P. Faraboschi, F. Gioachin, L. V. Kale, R. Kaufmann, B. Lee, V. March, D. Milojicic, and C. H. Suen. Evaluating and improving the performance and scheduling of hpc applications in cloud. IEEE Transactions on Cloud Computing, 4(3), 2016.
[9] M. Johnson, I. Zaretskaya, Y. Raytselis, Y. Merezhuk, S. McGinnis, and T. L. Madden. NCBI BLAST: a better web interface. Nucleic Acids Research, 36, 2008.
[10] G. Mariani, A. Anghel, R. Jongerius, and G. Dittmann. Predicting cloud performance for HPC applications: A user-oriented approach. In 2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, 2017.
[11] G. Mariani, A. Anghel, R. Jongerius, and G. Dittmann. Predicting cloud performance for HPC applications before deployment. Future Generation Computer Systems, 2018.
[12] G. Mariani, A. Anghel, R. Jongerius, and G. Dittmann. Predicting cloud performance for HPC applications before deployment. Future Generation Computer Systems, 87, 2018.
[13] M. A. S. Netto, R. L. F. Cunha, and N. Sultanum. Deciding when and how to move HPC jobs to the cloud. Computer, 48(11), 2015.
[14] S. Rani and O. P. Gupta. CLUS GPU-BLASTP: accelerated protein sequence alignment using GPU-enabled cluster. The Journal of Supercomputing, 2017.
[15] S. Sawyer, M. Horton, C. Burdyshaw, G. Brook, and B. Rekapalli. HPC-BLAST: Distributed BLAST for modern HPC clusters. In Proceedings of 11th International Conference on Bioinformatics and Computational Biology, 2019.
[16] S. Shi, Q. Wang, and P. Xu. Benchmarking state-of-the-art deep learning software tools. In 7th International Conference on Cloud Computing and Big Data, 2016.
[17] L. T. Yang, Xiaosong Ma, and F. Mueller. Cross-platform performance prediction of parallel applications using partial execution. In SC: Proceedings of the 2005 ACM/IEEE Conference on Supercomputing, 2005.
[18] M. Yoshimi, C. Wu, and T. Yoshinaga. Accelerating BLAST computation on an FPGAenhanced PC cluster. In 2016 Fourth International Symposium on Computing and Networking, 2016.
[19] W. Zhang, M. Hao, and M. Snir. Predicting HPC parallel program performance based on LLVM compiler. Cluster Computing, 20(2), 2017.
Publicado
08/11/2019
Como Citar
TAVARES, William; REIS, Lucas; BRUNETTA, Jeferson; BORIN, Edson.
Aplicação da técnica Paramount Iteration nas aplicações BLAST e DNN-ROM na nuvem computacional. In: SIMPÓSIO EM SISTEMAS COMPUTACIONAIS DE ALTO DESEMPENHO (SSCAD), 20. , 2019, Campo Grande.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 228-239.
DOI: https://doi.org/10.5753/wscad.2019.8671.
