Minicursos do XXIII Simpósio em Sistemas Computacionais de Alto Desempenho

Autores

Carla Osthoff (ed.)

LNCC

https://orcid.org/0000-0002-4694-7182

Daniel Cordeiro (ed.)

USP

https://orcid.org/0000-0003-4971-7355

DOI: https://doi.org/10.5753/sbc.10915.8

Sinopse

Neste livro estão compilados os seis minicursos apresentados durante o XXIII Simpósio em Sistemas Computacionais de Alto Desempenho (WSCAD 2022), realizado entre os dias 19 e 21 de outubro de 2022 em Florianópolis, SC. Em todos os minicursos destaca-se o viés prático que incentiva os participantes e os leitores a utilizarem alto desempenho de maneira efetiva. O capítulo 1 "OneAPI: Uma Abordagem para a Computação Heterogênea Centrada no Desenvolvedor", apresenta o padrão OneAPI, que é um padrão aberto para a aceleração de computação que adota uma abordagem single-source, na qual todo o código da aplicação pode ser especificado uniformemente usando a linguagem C++, independente de executar no host ou em aceleradores. O capítulo 2 "Inteligência Artificial e Função como Serviço: Provisionando Aplicações com o AWS Lambda", apresenta o AWS Lambda, que é uma das principais ferramentas do chamado serviço de computação sem servidor. Dentro desse contexto, são introduzidos os conceitos de Função como Serviço (FaaS), englobando o uso de AWS Lambda para provisionar aplicações de AI. O capítulo 3 "Coisas para Fazer antes de Paralelizar", apresenta práticas que devem ser feitas para melhorar o desempenho de um código antes de se pensar em paralelização e revisa conceitos essenciais para a avaliação e comparação de programas, incluindo exemplos e demonstrações ao vivo de como aplicá-los. O capítulo 4 "Fundamentos de Computação Acelerada com CUDA C/C++", ensina as ferramentas e técnicas fundamentais para acelerar aplicações escritas em linguagens C/C++ para execução em arquiteturas massivamente paralelas com CUDA. O capítulo 5 "From sequence assembly to ancestry testing: HPC challenges for bioinformatics" fornece uma visão introdutória do processo de obtenção até a análise de dados biológicos, mostrando os principais algoritmos e banco de dados em cada etapa e uma discussão sobre as formas de paralelização destes algoritmos e os principais desafios que ainda carecem de uma solução. O capítulo 6, "Ferramentas para Configuração e Gerenciamento de Cluster de Alto Desempenho em Nuvem Pública", apresenta as melhores práticas adotadas pelo NACAD-COPPE/UFRJ para a implantação de um cluster de Alto Desempenho usando uma Nuvem Pública focando no uso de ferramentas para implantar e gerenciar clusters de Computação de Alto Desempenho (HPC) na Nuvem AWS.

Capítulos:

1. OneAPI: uma Abordagem para a Computação Heterogênea Centrada no Desenvolvedor

Ricardo Menotti, Tiago da Conceição Oliveira

Capítulo 1

2. Inteligência Artificial e Função como Serviço: Provisionando Aplicações com o AWS Lambda

Sayonara S. Araújo, Francisco R. P. da Ponte, Victória T. Oliveira, Wendley S. da Silva, Dario Vieira, Miguel F. de Castro, Emanuel B. Rodrigues

Capítulo 2

3. Coisas para Fazer Antes de Paralelizar

Alfredo Goldman, Vitor Tessari Terra, Sarita Mazzini Bruschi

Capítulo 3

4. Fundamentos de Computação Acelerada com CUDA C/C++

Arthur Francisco Lorenzon

Capítulo 4

5. From Sequence Assembly to Ancestry Testing: HPC Challenges for Bioinformatics

Mariana Carmin, Cláudio Torres Júnior, André Ricardo Abed Grégio, Marco Antonio Zanata Alves

Capítulo 5

6. Ferramentas para Configuração e Gerenciamento de Cluster de Alto Desempenho em Nuvem Pública

Albino A. Aveleda, Alvaro L. G. A. Coutinho

Capítulo 6

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Referências

A. Botta, W. De Donato, V. Persico, and A. Pescapé, “Integration of cloud computing and internet of things: a survey,” Future generation computer systems, vol. 56, pp. 684–700, 2016.

A. Eivy and J. Weinman, “Be wary of the economics of "serverless" cloud computing,” IEEE Cloud Computing, vol. 4, no. 2, pp. 6–12, 2017.

A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam, “Mobilenets: Efficient convolutional neural networks for mobile vision applications,” arXiv preprint arXiv:1704.04861, 2017.

A. Khan and B. Hassan, “Dataset for forest fire detection,” Mendeley Data, V1, 2020. doi: 10.17632/gjmr63rz2r.1.

A. Krenker, J. Bešter, and A. Kos, “Introduction to the artificial neural networks,” Artificial Neural Networks: Methodological Advances and Biomedical Applications. InTech, pp. 1–18, 2011.

A. Lingayat, R. R. Badre, and A. K. Gupta, “Integration of linux containers in openstack: An introspection,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 12, no. 3, pp. 1094–1105, 2018.

A. Shrikumar, P. Greenside, and A. Kundaje, “Learning important features through propagating activation differences,” in International conference on machine learning, pp. 3145–3153, PMLR, 2017.

Alice Maria Giani, Guido Roberto Gallo, Luca Gianfranceschi, Giulio Formenti, Long walk to genomics: History and current approaches to genome sequencing and assembly. Computational and Structural Biotechnology Journal. Volume 18, 2020, Pages 9-19

Amazon Web Service, “AWS ParallelCluster: AWS ParallelCluster User Guide”, July 6, 2022.

Amazon, “Aws lambda: Execute código sem se preocupar com servidores ou clusters.” https://aws.amazon.com/lambda/, 2022. Online; acessado em Ago 2022.

Anandika Dhaliwal, 2013. DNA Extraction and Purification. Materials and Methods volume 3.

Ansible Red Hat. Página inicial. Disponível em: https://www.ansible.com/. Acesso em: 15/08/2022.

Anthony Rhoads, Kin Fai Au, PacBio Sequencing and Its Applications, Genomics, Proteomics Bioinformatics, Volume 13, Issue 5, 2015, Pages 278-289, ISSN 1672-0229, https://doi.org/10.1016/j.gpb.2015.08.002.

AWS services used by AWS ParallelCluster. Disponível em: [link]. Acesso em: 15/08/2022.

B. Bermejo, C. Juiz, and C. Guerrero, “Virtualization and consolidation: a systematic review of the past 10 years of research on energy and performance,” The Journal of Supercomputing, vol. 75, no. 2, pp. 808–836, 2019.

B. King, “What is faas? function as a service explained.” [link], 2022. Online; acessado em Ago 2022.

B. M. Abbott, A security evaluation methodology for container images. Brigham Young University, 2017.

B. P. Rimal, E. Choi, and I. Lumb, “A taxonomy and survey of cloud computing systems,” in 2009 Fifth International Joint Conference on INC, IMS and IDC, pp. 44–51, Ieee, 2009.

Baldi, P. and Brunak, S., 2001. bioinformatics: the machine learning approach. MIT press.

Boyer, M., Meng, J., & Kumaran, K. (2013). Improving GPU Performance Prediction with Data Transfer Modeling. 2013 IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum, 1097–1106. https://doi.org/10.1109/IPDPSW.2013.236

Bruel, P., Amaris Gonzalez, M., and Goldman, A. (2017). Autotuning cuda compiler parameters for heterogeneous applications using the opentuner framework. Concurrency and Computation Practice and Experience, 29.

Brzinsky-Fay, Christian, and Ulrich Kohler. "New developments in sequence analysis." Sociological methods research 38.3 (2010): 359-364.

Butenhof, David R. Programming with POSIX threads. Addison-Wesley Professional, 1997.

By Erekevan - Own work, CC BY-SA 4.0, https://commons.wikimedia.org/w/index.php?curid=116777958

C. Anderson, “Docker [software engineering],” IEEE Software, vol. 32, no. 3, pp. 102–c3, 2015.

C. elegans Sequencing Consortium, 1998. Genome sequence of the nematode C. elegans: a platform for investigating biology. Science (New York, N.Y.), 282(5396), 2012–2018.

C. Namespaces-Overview, “Cgroup namespaces-overview of linux cgroup namespaces oct..” [link], 2021. Online; acessado em Ago 2022.

C. Pahl, “Containerization and the paas cloud,” IEEE Cloud Computing, vol. 2, no. 3, pp. 24–31, 2015.

Carvalho, Antonio Bernardo and Dupim, Eduardo G. and Nassar, Gabriel, 2016. Improved assembly of noisy long reads by k-mer validation. Cold Spring Harbor Laboratory.

Ceissler, C., Nepomuceno, R., Pereira, M., & Araujo, G. (2018). Automatic Offloading of Cluster Accelerators. IEEE 26th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM), 224–224.

Chandra, R., Dagum, L., Kohr, D., Menon, R., Maydan, D., and McDonald, J. (2001). Parallel programming in OpenMP. Morgan kaufmann.

Chandra, Rohit, et al. Parallel programming in OpenMP. Morgan kaufmann, 2001.

Chef. Página inicial. Disponível em: https://www.chef.io/. Acesso em: 15/08/2022.

Chien, S., Peng, I., and Markidis, S. (2019). Performance evaluation of advanced features in cuda unified memory. In 2019 IEEE/ACM Workshop on Memory Centric High Performance Computing (MCHPC), pages 50–57. IEEE.

Clark, Dominic A., Geoffrey J. Barton, and Christopher J. Rawlings. "A knowledgebased architecture for protein sequence analysis and structure prediction." Journal of Molecular Graphics 8.2 (1990): 94-107.

Costa, Gustavo GL, et al. "The mitochondrial genome of Moniliophthora roreri, the frosty pod rot pathogen of cacao." Fungal Biology 116.5 (2012): 551-562.

Cristianini, N. and Hahn, M.W., 2006. Introduction to computational genomics: a case studies approach. Cambridge University Press.

Crockett, L. H., Elliot, R. A., Enderwitz, M. A., & Stewart, R. W. (2014). The Zynq Book: Embedded Processing with the Arm Cortex-A9 on the Xilinx Zynq-7000 All Programmable Soc. Strathclyde Academic Media.

Crossplane. Página inicial. Disponível em: https://crossplane.io/. Acesso em: 15/08/2022.

D. Crevier, AI: the tumultuous history of the search for artificial intelligence. Basic Books, Inc., 1993.

Docker, “Dockerfile reference.” [link], 2021. Online; acessado em Ago 2022.

Docker, “What is a container.” [link], 2021. Online; acessado em Ago 2022.

Dr. Josh Wang, Advancing genomics, medicine and health together – by semiconductor DNA synthesis technology, [link].

Ekblom, R. and Wolf, J.B.W. (2014), A field guide to whole-genome sequencing, assembly and annotation. Evol Appl, 7: 1026-1042

Expósito, R. R., J. Veiga, J. González Domínguez and J. Touriño, 2017. Mardre: Efficient mapreduce based removal of duplicate DNA reads in the cloud. Bioinformatics, 33(17): 2762 - 2764.

Farber, R. (2016). Parallel Programming with OpenACC. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 1st edition.

Farrar M (2007) Striped smith-waterman speeds database searches six times over other simd implementations. Bioinformatics 23(2):156–161. https://doi.org/10.1093/bioinformatics/btl582

Firmansyah, I., & Yamaguchi, Y. (2019). OpenCL Implementation of FPGA-Based Signal Generation and Measurement. IEEE Access, 7, 48849–48859.

Gauthier, Michel. (2007). Simulation of polymer translocation through small channels: A molecular dynamics study and a new Monte Carlo approach.

Gautier, Q., Althoff, A., Pingfan Meng & Kastner, R. (2016). Spector: An OpenCL FPGA benchmark suite. 2016 International Conference on Field-Programmable Technology (FPT), 141–148.

Goldman, A., Uhura, E., and Bruschi, S. (2022). Coisas para saber antes de fazer o seu próprio Benchmarks Game. In Lorenzon, A., Castro, M., and Pillon, M., editors, Minicursos da XXII Escola Regional de Alto Desempenho da Região Sul.

Gollery, M., 2005. Bioinformatics: sequence and genome analysis. Clinical Chemistry, 51(11), pp.2219-2220.

Gut, I.G. New sequencing technologies. Clin Transl Oncol 15, 879–881 (2013). https://doi.org/10.1007/s12094-013-1073-6

H. Zwart (2015) “Human Genome Project: history and assessment”. In: International Encyclopedia of Social Behavioral Sciences. 2nd ed. Oxford: Elsevier, 311–317.

Han, J. and Sharma, B. (2019). Learn CUDA Programming: A beginner’s guide to GPU programming and parallel computing with CUDA 10.x and C/C++. Packt Publishing.

Henikoff, Steven, and Jorja G. Henikoff. "Amino acid substitution matrices from protein blocks." Proceedings of the National Academy of Sciences 89.22 (1992): 10915-10919.

Hennessy, J. L., & Patterson, D. A. (2019). A New Golden Age for Computer Architecture. Commun. ACM, 62(2), 48–60. https://doi.org/10.1145/3282307

Hoos, H. H. (2012). Programming by optimization. Commun. ACM, 55(2):70–80.

Hu, Y., Colantonio, V., Müller, B.S.F. et al. Genome assembly and population genomic analysis provide insights into the evolution of modern sweet corn. Nat Commun 12, 1227 (2021). https://doi.org/10.1038/s41467-021-21380-4

I. Baldini, P. Castro, K. Chang, P. Cheng, S. Fink, V. Ishakian, N. Mitchell, V. Muthusamy, R. Rabbah, A. Slominski, et al., “Serverless computing: Current trends and open problems,” in Research advances in cloud computing, pp. 1–20, Springer, 2017.

I. C. Education, “Faas (function-as-a-service).” https://www.ibm.com/cloud/learn/faas, 2022. Online; acessado em Ago 2022.

I. Red Hat, “Cloud computing.” https://www.redhat.com/pt-br/topics/cloud, 2018. Online; acessado em Ago 2022.

Immy Mobley, 2021. [link].

Intel Corp. (2020a). FPGA Add-On for oneAPI Base Toolkit(Beta). [link].

Intel Corp. (2020b). Intel oneAPI Toolkits(Beta). [link]

Intel Corp. (2020c). Intel Stratix 10 FPGAs. [link].

J. M. Hellerstein, J. Faleiro, J. E. Gonzalez, J. Schleier-Smith, V. Sreekanti, A. Tumanov, and C.Wu, “Serverless computing: One step forward, two steps back,” 2018.

J. Zenisek, F. Holzinger, and M. Affenzeller, “Machine learning based concept drift detection for predictive maintenance,” Computers & Industrial Engineering, vol. 137, p. 106031, 2019.

Jain, R. (1991). The art of computer systems performance analysis: techniques for experimental design, measurement, simulation, and modeling. Wiley New York.

James M. Heather, Benjamin Chain. The sequence of sequencers: The history of sequencing DNA, Genomics, Volume 107, Issue 1, 2016, Pages 1-8.

Jones, N.C. and Pevzner, P.A., 2004. An introduction to bioinformatics algorithms. MIT press.

Jorde, Lynn B., and Michael J. Bamshad. "Genetic ancestry testing: What is it and why is it important?." JAMA 323.11 (2020): 1089-1090.

Junking, S. (2015). The Compute Architecture of Intel® Processor Graphics Gen9. Recuperado setembro 2, 2022, de [link].

K. K. Mohbey and S. Kumar, “The impact of big data in predictive analytics towards technological development in cloud computing,” International Journal of Engineering Systems Modelling and Simulation, vol. 13, no. 1, pp. 61–75, 2022.

K. Kaur, T. Dhand, N. Kumar, and S. Zeadally, “Container-as-a-service at the edge: Trade-off between energy efficiency and service availability at fog nano data centers,” IEEE wireless communications, vol. 24, no. 3, pp. 48–56, 2017.

K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv:1409.1556, 2014.

Kalibera, T. and Jones, R. (2013). Rigorous benchmarking in reasonable time. In Proceedings of the 2013 International Symposium on Memory Management, ISMM ’13, page 63–74, New York, NY, USA. Association for Computing Machinery.

Keras. URL https://keras.io/. Acessado: 14/08/2022.

Keryell, R., & Yu, L.-Y. (2018). Early Experiments Using SYCL Single-Source Modern C++ on Xilinx FPGA: Extended Abstract of Technical Presentation. Proceedings of the International Workshop on OpenCL. https://doi.org/10.1145/3204919.3204937

Khronos Group. (2020a). SYCL 1.2.1 API Reference Guide. https://www.khronos.org/files/sycl/sycl-121-reference-guide.pdf

Khronos Group. (2020b). SYCL Specification. https://www.khronos.org/sycl/

Klaczko, Louis B., and Blanche C. Bitner-Mathe. "On the edge of a wing." Nature 346.6282 (1990): 321-321.

Knap, M. and Czarnul, P. (2019). Performance evaluation of unified memory with prefetching and oversubscription for selected parallel cuda applications on nvidia pascal and volta gpus. The Journal of Supercomputing, 75(11):7625–7645.

Knorst, T., Jordan, M. G., Lorenzon, A. F., Rutzig, M. B., and Beck, A. C. S. (2021). Etcf – energy-aware cpu thread throttling and workload balancing framework for cpu-fpga collaborative environments. In 2021 XI Brazilian Symposium on Computing Systems Engineering (SBESC), pages 1–8.

L. Benedicic, F. A. Cruz, A. Madonna, and K. Mariotti, “Sarus: Highly scalable docker containers for hpc systems,” in International Conference on High Performance Computing, pp. 46–60, Springer, 2019.

Landaverde, R., Zhang, T., Coskun, A. K., and Herbordt, M. (2014). An investigation of unified memory access performance in cuda. In 2014 IEEE High Performance Extreme Computing Conference (HPEC), pages 1–6. IEEE.

Lehninger, A.L., Nelson, D.L., Cox, M.M. and Cox, M.M., 2005. Lehninger principles of biochemistry. Macmillan.

Leiserson, C. E., Thompson, N. C., Emer, J. S., Kuszmaul, B. C., Lampson, B. W., Sanchez, D., & Schardl, T. B. (2020). There’s plenty of room at the Top: What will drive computer performance after Moore’s law? Science, 368(6495), eaam9744. https://doi.org/10.1126/science.aam9744

Li H (2018) Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics 34(18):3094–3100. https://doi.org/10.1093/bioinformatics/bty191

Li, Kuo-Bin. "ClustalW-MPI: ClustalW analysis using distributed and parallel computing." Bioinformatics 19.12 (2003): 1585-1586.

Lustre. Página inicial. Disponível em: https://www.lustre.org. Acesso em: 15/08/2022.

M. Chadha, A. Jindal, and M. Gerndt, “Towards federated learning using faas fabric,” in Proceedings of the 2020 Sixth International Workshop on Serverless Computing, WoSC’20, (New York, NY, USA), p. 49–54, Association for Computing Machinery, 2020.

M. Villamizar, O. Garcés, L. Ochoa, H. Castro, L. Salamanca, M. Verano Merino, R. Casallas, S. Gil, C. Valencia, A. Zambrano, and M. Lang, “Cost comparison of running web applications in the cloud using monolithic, microservice, and aws lambda architectures,” Service Oriented Computing and Applications, vol. 11, 06 2017.

M. Z. Alom, T. Taha, C. Yakopcic, S. Westberg, P. Sidike, M. Nasrin, M. Hasan, B. Essen, A. Awwal, and V. Asari, “A state-of-the-art survey on deep learning theory and architectures,” Electronics, vol. 8, p. 292, 03 2019.

Ma, Jianzhu, and Sheng Wang. "Algorithms, applications, and challenges of protein structure alignment." Advances in Protein Chemistry and Structural Biology 94 (2014): 121-175.

Mbakoyiannis, D., Tomoutzoglou, O., & Kornaros, G. (2018). Energy-Performance Considerations for Data Offloading to FPGA-Based Accelerators Over PCIe. ACM Trans. Archit. Code Optim., 15(1). https://doi.org/10.1145/3180263

Menotti, R. (2010). LALP: uma linguagem para exploração do paralelismo de loops em computação reconfigurável [tese de dout., Universidade de São Paulo]. https://doi.org/10.11606/T.55.2010.tde-17082010-151100

Mikhailov,D., Cofer,H. and Gomperts,R. (2001) Performance optimization of Clustal W: parallel Clustal W, HT Clustal, and MULTICLUSTAL, White papers, Silicon Graphics, Mountain View, CA

Morisse, Pierre and Marchet, Camille and Limasset, Antoine and Lecroq, Thierry and Lefebvre, Arnaud, 2020, CONSENT: Scalable long read self-correction and assembly polishing with multiple sequence alignment, bioRxiv - Cold Spring Harbor Laboratory. https://doi.org/10.1101/546630

Morisse, Pierre and Marchet, Camille and Limasset, Antoine and Lecroq, Thierry and Lefebvre, Arnaud, 2020. ONSENT: Scalable long read self-correction and assembly polishing with multiple sequence alignment. Cold Spring Harbor Laboratory.

MVAPICH: MPI over InfiniBand, Omni-Path, Ethernet/iWARP, and RoCE. Disponível em: https://mvapich.cse.ohio-state.edu/downloads/. Acesso em: 15/08/2022.

Namespaces-Overview, “Namespaces-overview of linux namespaces oct..” https://man7:org/linux/man-pages/man7/namespaces:7:html, 2021. Online; acessado em Ago 2022.

Navarro, A., Lorenzon, A. F., Ayguad´e, E., and Beltran, V. (2020). Enhancing resource management through prediction-based policies. In Malawski, M. and Rzadca, K., editors, Euro-Par 2020: Parallel Processing, pages 493–509, Cham. Springer International Publishing.

NVIDIA. (2022). CUDA C++ Programming Guide. https://docs.nvidia.com/cuda/cudac-programming-guide/

Open MPI: Open Source High Performance Computing. Disponível em: https://www.open-mpi.org/. Acesso em: 15/08/2022.

Oracle, “O que é inteligência artificial (ia)? saiba mais sobre inteligência artificial.” https://www.oracle.com/br/artificial-intelligence/what-is-ai/, 2022. Online; acessado em Ago 2022.

P. Joshi, Artificial intelligence with python. Packt Publishing Ltd, 2017.

P. Linardatos, V. Papastefanopoulos, and S. Kotsiantis, “Explainable ai: A review of machine learning interpretability methods,” Entropy, vol. 23, no. 1, 2021.

P. Liu, S. Ji, L. Fu, K. Lu, X. Zhang,W.-H. Lee, T. Lu,W. Chen, and R. Beyah, “Understanding the security risks of docker hub,” in European Symposium on Research in Computer Security, pp. 257–276, Springer, 2020.

P. Mell, T. Grance, et al., “The nist definition of cloud computing,” 2011.

P. R. Desai, “A survey of performance comparison between virtual machines and containers,” Int. J. Comput. Sci. Eng, vol. 4, no. 7, pp. 55–59, 2016.

PacBio, New Chemistry Boosts Average Read Length to 10 kb – 15 kb for PacBio® RS II, 2014, [link]

ParallelCluster Manager - Make HPC Easy. Disponível em: [link]. Acesso em: 15/08/2022.

Preetha J. Shetty, 2020. The Evolution of DNA Extraction Methods. American Journal of Biomedical Science & Research.

Pulumi. Página inicial. Disponível em: https://www.pulumi.com/. Acesso em: 15/08/2022.

Puppet. Página inicial. Disponível em: https://puppet.com/. Acesso em: 15/08/2022.

PyTorch. URL https://pytorch.org/. Acessado: 14/08/2022.

Q. Zhang, L. Liu, C. Pu, Q. Dou, L. Wu, and W. Zhou, “A comparative study of containers and virtual machines in big data environment,” in 2018 IEEE 11th International Conference on Cloud Computing (CLOUD), pp. 178–185, IEEE, 2018.

R. Alshamrani and X. Ma, “Deep learning,” por Laurie A. Schintler y Connie L. McNeely. Cham: Springer International Publishing, pp. 1–5, 2019.

R. Alshamrani and X. Ma, Deep Learning, pp. 1–5. Cham: Springer International Publishing, 2019.

R. Bala, B. Gill, D. Smith, K. Ji, and D. Wright, “Quadrante mágico para infraestrutura em nuvem e serviços de plataforma.” [link], 2021. Online; acessado em Jun 2022.

R. Dua, A. R. Raja, and D. Kakadia, “Virtualization vs containerization to support paas,” in 2014 IEEE International Conference on Cloud Engineering, pp. 610–614, IEEE, 2014.

R. S. Michalski, J. G. Carbonell, and T. M. Mitchell, Machine learning: An artificial intelligence approach. Springer Science & Business Media, 2013.

Rahn R, Budach S, Costanza P, Ehrhardt M, Hancox J, Reinert K (2018) Generic accelerated sequence alignment in seqan using vectorization and multi-threading. Bioinformatics 34(20):3437– 3445. https://doi.org/10.1093/bioinformatics/bty380

Reinders, J., Ashbaugh, B., Brodman, J., Kinsner, M., Pennycook, J., & Tian, X. (2020). Data Parallel C++: Mastering DPC++ for Programming of Heterogeneous Systems using C++ and SYCL. Apress.

Rognes T (2011) Faster smith-waterman database searches with inter-sequence simd parallelisation. BMC Bioinform 12(1):1–11. https://doi.org/10.1186/1471-2105-12-221

Rognes T, Seeberg E (2000) Six-fold speed-up of smith-waterman sequence database searches using parallel processing on common microprocessors. Bioinformatics 16(8):699–706. https://doi.org/10.1093/bioinformatics/16.8.699

S. A. Babu, M. Hareesh, J. P. Martin, S. Cherian, and Y. Sastri, “System performance evaluation of para virtualization, container virtualization, and full virtualization using xen, openvz, and xenserver,” in 2014 fourth international conference on advances in computing and communications, pp. 247–250, IEEE, 2014.

S. Alla and S. K. Adari, “What is mlops?,” in Beginning MLOps with MLFlow, pp. 79–124, Springer, 2021.

S. K. R and J. Lakshmi, “Qos aware faas platform,” in 2021 IEEE/ACM 21st International Symposium on Cluster, Cloud and Internet Computing (CCGrid), pp. 812– 819, 2021.

S. Mäkinen, H. Skogström, E. Laaksonen, and T. Mikkonen, “Who needs mlops: What data scientists seek to accomplish and how can mlops help?,” in 2021 IEEE/ ACM 1st Workshop on AI Engineering - Software Engineering for AI (WAIN), pp. 109–112, 2021.

S. Sagiroglu and D. Sinanc, “Big data: A review,” in 2013 international conference on collaboration technologies and systems (CTS), pp. 42–47, IEEE, 2013.

S. Vaucher, R. Pires, P. Felber, M. Pasin, V. Schiavoni, and C. Fetzer, “Sgx-aware container orchestration for heterogeneous clusters,” in 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS), pp. 730–741, IEEE, 2018.

Sanders, J. and Kandrot, E. (2010). CUDA by example: an introduction to general-purpose GPU programming. Addison-Wesley Professional.

Sergey Nurk, et al. The complete sequence of a human genome (2022). Science 376(6588):44-53. https://doi.org/10.1126/science.abj6987

SLURM. Disponível em: https://slurm.schedmd.com/. Acesso em: 15/08/2022.

Smith T.F.,Waterman M.S., and Burks C. 1985. The statistical distribution of nucleic acid similarities. Nucleic Acids Res. 13:645-656.

Spot Instance Advisor. Disponível em: [link]. Acesso em: 15/08/2022.

Star Cluster. Disponível em: http://star.mit.edu/cluster/

Stevens, Tim J., and Wayne Boucher. Python programming for biology. Cambridge University Press, 2015.

Stock, F.-W. (2019). Rapid Prototyping and Exploration Environment for Generating Cto-Hardware-Compilers [tese de dout., Technische Universität]. Darmstadt.

Stone, J. E., Gohara, D., and Shi, G. (2010). Opencl: A parallel programming standard for heterogeneous computing systems. Computing in Science and Engg., 12(3):66–73.

Stroustrup, B. (2013). The C++ programming language. Pearson Education.

Sutter, H. (2011). Welcome to the Jungle. http://herbsutter.com/welcome-to-the-jungle

Suzuki H, Kasahara M (2018) Introducing difference recurrence relations for faster semi-global alignment of long sequences. BMC Bioinform 19(1):33–47. https://doi.org/10.1186/s12859-018-2014-8

T. Elgamal, “Costless: Optimizing cost of serverless computing through function fusion and placement,” in 2018 IEEE/ACM Symposium on Edge Computing (SEC), pp. 300–312, 2018.

T. Elgamal, A. Sandur, K. Nahrstedt, and G. Agha, “Costless: Optimizing cost of serverless computing through function fusion and placement,” in 2018 IEEE/ACM Symposium on Edge Computing (SEC), pp. 300–312, 2018.

T. Lynn, P. Rosati, A. Lejeune, and V. Emeakaroha, “A preliminary review of enterprise serverless cloud computing (function-as-a-service) platforms,” in 2017 IEEE International Conference on Cloud Computing Technology and Science (Cloud-Com), pp. 162–169, 2017.

TensorFlow. URL https://www.tensorflow.org/?hl=pt-br. Acessado: 14/08/2022.

Terraform. Página inicial. Disponível em: https://www.terraform.io/. Acesso em: 15/08/2022.

Thompson,J.D., Higgins,D.G. and Gibson,T.J. (1994) CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res., 22, 4673–4680.

V. H. Phung and E. J. Rhee, “A high-accuracy model average ensemble of convolutional neural networks for classification of cloud image patches on small datasets,” Applied Sciences, vol. 9, no. 21, 2019.

V. Ishakian, V. Muthusamy, and A. Slominski, “Serving deep learning models in a serverless platform,” in 2018 IEEE International Conference on Cloud Engineering (IC2E), pp. 257–262, 2018.

V. Rajaraman, “Johnmccarthy—father of artificial intelligence,” Resonance, vol. 19, no. 3, pp. 198–207, 2014.

Watson, James D. "The human genome project: past, present, and future." Science 248.4951 (1990): 44-49.

Wenger, A.M., Peluso, P., Rowell, W.J. et al. Accurate circular consensus longread sequencing improves variant detection and assembly of a human genome. Nat Biotechnol 37, 1155–1162 (2019). https://doi.org/10.1038/s41587-019-0217-9

Wozniak A (1997) Using video-oriented instructions to speed up sequence comparison. Bioinformatics 13(2):145–150. https://doi.org/10.1093/bioinformatics/13.2.145

Xia, Zeyu, et al. "A review of parallel implementations for the smith–waterman algorithm." Interdisciplinary Sciences: Computational Life Sciences (2021): 1-14.

Xiao, Tiantian Zhou, Wenhao. (2020). The third generation sequencing: The advanced approach to genetic diseases. Translational Pediatrics. 9. 163-173. 10.21037/tp.2020.03.06.

Xilinx Inc. (2020). Virtex UltraScale+ VU19P FPGA. https://xilinx.com/vu19p

Xu, H., X. Luo, J. Qian, X. Pang, J. Song, G. Qian, J. Chen and S. Chen, 2012. Fastuniq: A fast de novo duplicates removal tool for paired short reads. PloS one, 7(12): e52249.

Z. Li, M. Kihl, Q. Lu, and J. A. Andersson, “Performance overhead comparison between hypervisor and container based virtualization,” in 2017 IEEE 31st International Conference on advanced information networking and applications (AINA), pp. 955–962, IEEE, 2017.

Zahran, M. (2017). Heterogeneous Computing: Here to Stay. Commun. ACM, 60(3), 42–45. https://doi.org/10.1145/3024918

Zomaya, A.Y., 2005. Parallel computing for bioinformatics and computational biology. Wiley.

Zone, N. D. (2019). Cuda toolkit documentation. NVIDIA, [Online]. Available: http://docs.nvidia.com/cuda/cuda-c-best-practices-guide/#occupancy. [Acedido em Fevereiro 2015].