Minicursos da XXII Escola Regional de Alto Desempenho da Região Sul

Autores

Arthur Lorenzon (ed.)

UNIPAMPA

Márcio Castro (ed.)

UFSC

Mauricio Pillon (ed.)

UDESC

DOI: https://doi.org/10.5753/sbc.8387.0

Sinopse

O Livro de Minicursos apresentados na XXII Escola Regional de Alto Desempenho da Região Sul (ERAD/RS) consolida a integração de pesquisadores em programação paralela da Região Sul do Brasil. Constituído de cinco capítulos, todos os temas com foco em desempenho, o livro possui uma variedade de assuntos convergentes.

No primeiro capítulo, intitulado "Desenvolvimento de Aplicações Paralelas Adaptativas: Uma Visão de Duas Décadas (2001-2021)", os autores tratam da evolução das arquiteturas paralelas e adequações de seus usos em ambientes dinâmicos. O tema provoca uma reflexão interessante tanto para pesquisadores já consolidados quanto para jovens alunos em busca de novos desafios.

Nos capítulos dois e três, os autores discorrem sobre linguagens e técnicas de programação em computação paralela, são eles: "Computação de Alto Desempenho em Julia", que apresenta uma linguagem moderna, de fácil uso e aprendizado; e "Introdução à Programação com Memória Persistente", que discuste sobre motivação e problemas de consistência de dados em processadores com memória persistente (PM).

No quarto capítulo, os autores de "Coisas para saber antes de fazer o seu próprio Benchmarks Game" sumarizam os aspectos essenciais a avaliação e comparação de programas com foco na área de Jogos.

O desfecho do livro destaca um tema onipresente em pesquisas de processamento de alto desempenho (PAD), o capítulo "Apresentação de Resultados Experimentais para Processamento de Alto Desempenho em R", que discute formas de melhor dispor os dados em textos científicos e a reprodutibilidade de experimentos.

Capítulos:

1. Desenvolvimento de Aplicações Paralelas Adaptativas: Uma Visão de Duas Décadas (2001-2021)

Guilherme Galante, Rodrigo da Rosa Righi

DOI: https://doi.org/10.5753/sbc.8387.0.1

Capítulo 1

2. High Performance Computing in Julia

Roberto Machado Velho, Rafael Benchimol Klausner, Matheus da Silva Serpa, Adriano Maurício de Almeida Côrtes

DOI: https://doi.org/10.5753/sbc.8387.0.2

Capítulo 2

3. Introdução à Programação com Memória Persistente

Alexandro Baldassin, Emilio Francesquini

DOI: https://doi.org/10.5753/sbc.8387.0.3

Capítulo 3

4. Coisas para Saber Antes de Fazer o Seu Próprio Benchmarks Game

Alfredo Goldman, Elisa Uhura, Sarita Mazzini Bruschi

DOI: https://doi.org/10.5753/sbc.8387.0.4

Capítulo 4

5. Apresentação de Resultados Experimentais para Processamento de Alto Desempenho em R

Vinícius Garcia Pinto, Lucas Leandro Nesi, Lucas Mello Schnorr

DOI: https://doi.org/10.5753/sbc.8387.0.5

Capítulo 5

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Referências

Akerman, J. (2005). Toward a universal memory. Science, 308(5721):508-510.

Al-khafajiy, M., Baker, T., Al-Libawy, H., Maamar, Z., Aloqaily, M., and Jararweh, Y. (2019). Improving fog computing performance via fog-2-fog collaboration. Future Generation Computer Systems, 100:266 - 280.

Aldinucci, M., Coppola, M., Danelutto, M., Tonellotto, N., Vanneschi, M., and Zoccolo, C. (2006). High level grid programming with ASSIST. Computational Methods in Science and Technology, 12(1):21-32.

Bache, S. M. and Wickham, H. (2022). magrittr: A Forward-Pipe Operator for R. R package version 2.0.2.

Baldassin, A., Barreto, J. a., Castro, D., and Romano, P. (2021). Persistent memory: A survey of programming support and implementations. ACM Comput. Surv., 54(7).

Batheja, J. and Parashar, M. (2003). A framework for adaptive cluster computing using javaspaces. Cluster Computing, 6(3):201-213.

Boukhobza, J., Rubini, S., Chen, R., and Shao, Z. (2018). Emerging NVM: A Survey on Architectural Integration and Research Challenges. ACM Trans. Des. Autom. Electron. Syst., 23(2):1-32.

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.

Buisson, J., Andre, F., and Pazat, J.-L. (2007). Supporting adaptable applications in grid resource management systems. In Proceedings of the 8th IEEE/ACM International Conference on Grid Computing, GRID '07, page 58-65, USA. IEEE Computer Society.

Burr, G., Kurdi, B., Scott, J., Lam, C., Gopalakrishnan, K., and Shenoy, R. (2008). Overview of candidate device technologies for storage-class memory. IBM Journal of Research and Development, 52(4.5):449-464.

Carrijo Nasciutti, T., Panetta, J., and Pais Lopes, P. (2019). Evaluating optimizations that reduce global memory accesses of stencil computations in gpgpus. Concurrency and Computation: Practice and Experience, 31(18):e4929. páginas

Catalán, S., Herrero, J. R., Quintana-Ortí, E. S., Rodríguez-Sánchez, R., and Van De Geijn, R. (2019). A case for malleable thread-level linear algebra libraries: The lu factorization with partial pivoting. IEEE Access, 7:17617- 17633.

Cera, M. C. (2011). Providing adaptability to MPI applications on current parallel architectures. PhD thesis, Universidade Federal do Rio Grande do Sul. Instituto de Informática. Programa de Pós-Graduação em Computação.

Chen, E. and Yen, T. (2009). Comparing SLC and MLC Flash Technologies and Structure.

Chen, Y., Chang, Y., Chen, C., Lin, Y., Chen, J., and Chang, Y. (2017). Cloud-fog computing for information-centric internet-of-things applications. In 2017 International Conference on Applied System Innovation (ICASI), pages 637-640.

Cho, Y., Guzman, C. A. C., and Egger, B. (2018). Maximizing system utilization via parallelism management for co-located parallel applications. In Proceedings of the 27th International Conference on Parallel Architectures and Compilation Techniques, PACT '18, pages 1-14, New York, NY, USA. Association for Computing Machinery.

Choi, Y., Alsaffar, A. A., , Pham, H. P., Hong, C., Huh, E., and Aazam, M. (2016). An Architecture of IoT Service Delegation and Resource Allocation Based on Collaboration between Fog and Cloud Computing. Mobile Information Systems, 2016.

Comprés, I., Mo-Hellenbrand, A., Gerndt, M., and Bungartz, H.-J. (2016). Infrastructure and api extensions for elastic execution of mpi applications. In Proceedings of the 23rd European MPI Users' Group Meeting, EuroMPI 2016, page 82-97, New York, NY, USA. Association for Computing Machinery.

Creech, T. M. (2015). Efficient multiprogramming for multicores with scaf. Master's thesis, Faculty of the Graduate School of the University of Maryland.

da Rosa Righi, R., Rodrigues, V. F., da Costa, C. A., Galante, G., Bona, L. C. E. D., and Ferreto, T. C. (2016). Autoelastic: Automatic resource elasticity for high performance applications in the cloud. IEEE Trans. Cloud Comput., 4(1):6-19.

D'Amico, M., Garcia-Gasulla, M., López, V., Jokanovic, A., Sirvent, R., and Corbalan, J. (2018). Drom: Enabling efficient and effortless malleability for resource managers. In Proceedings of the 47th International Conference on Parallel Processing Companion, ICPP '18, pages 1-10, New York, NY, USA. Association for Computing Machinery.

Dagostini, J. I., Pinto, V. G., Nesi, L. L., and Schnorr, L. M. (2021). Are you root? Experimentos Reprodutíveis em Espaço de Usuário. In Charão, A. and Serpa, M., editors, Minicursos da XXI Escola Regional de Alto Desempenho da Região Sul, chapter 3, pages 70-87. Sociedade Brasileira de Computação - SBC, Porto Alegre.

Doh, I. H., Kim, Y. J., Park, J. S., Kim, E., Choi, J., Lee, D., and Noh, S. H. (2010). Towards Greener Data Centers With Storage Class Memory: Minimizing Idle Power Waste Through Coarse-Grain Management In Fine-Grain Scale. In Conference On Computing Frontiers.

Dominico, S., de Almeida, E. C., Meira, J. A., and Alves, M. A. (2018). An elastic multi-core allocation mechanism for database systems. In 2018 IEEE 34th International Conference on Data Engineering (ICDE), pages 473-484.

Dongarra, J., Beckman, P., Moore, T., Aerts, P., Aloisio, G., Andre, J.-C., Barkai, D., Berthou, J.-Y., Boku, T., Braunschweig, B., Cappello, F., Chapman, B., Chi, X., Choudhary, A., Dosanjh, S., Dunning, T., Fiore, S., Geist, A., Gropp, B., Harrison, R., Hereld, M., Heroux, M., Hoisie, A., Hotta, K., Jin, Z., Ishikawa, Y., Johnson, F., Kale, S., Kenway, R., Keyes, D., Kramer, B., Labarta, J., Lichnewsky, A., Lippert, T., Lucas, B., Maccabe, B., Matsuoka, S., Messina, P., Michielse, P., Mohr, B., Mueller, M. S., Nagel, W. E., Nakashima, H., Papka, M. E., Reed, D., Sato, M., Seidel, E., Shalf, J., Skinner, D., Snir, M., Sterling, T., Stevens, R., Streitz, F., Sugar, B., Sumimoto, S., Tang, W., Taylor, J., Thakur, R., Trefethen, A., Valero, M., Van Der Steen, A., Vetter, J., Williams, P., Wisniewski, R., and Yelick, K. (2011). The international exascale software project roadmap. Int. J. High Perform. Comput. Appl., 25(1):3-60.

El Maghraoui, K., Desell, T. J., Szymanski, B. K., and Varela, C. A. (2009). Malleable iterative mpi applications. Concurrency and Computation: Practice and Experience, 21(3):393-413.

Eshraghian, K. (2010). Evolution of Nonvolatile Resistive Switching Memory Technologies: The Related Influence on Hetrogeneous Nanoarchitectures. In Transactions on Electrical and Electronic Materials.

Feitelson, D. G. and Rudolph, L. (1996). Toward convergence in job schedulers for parallel supercomputers. In Feitelson, D. G. and Rudolph, L., editors, Job Scheduling Strategies for Parallel Processing, volume 1162 of Lecture Notes in Computer Science, pages 1-26. Springer-Verlag.

Firth, S. (2014). The machine - HP labs launches a bold new research initiative to transform the future of computing.

Foster, I., Zhao, Y., Raicu, I., and Lu, S. (2008). Cloud computing and grid computing 360-degree compared. In 2008 Grid Computing Environments Workshop, pages 1-10. IEEE.

Fox, W., Ghoshal, D., Souza, A., Rodrigo, G. P., and Ramakrishnan, L. (2017). E-hpc: A library for elastic resource management in hpc environments. In Proceedings of the 12th Workshop on Workflows in Support of Large-Scale Science, WORKS '17, pages 1-11, New York, NY, USA. Association for Computing Machinery.

Galante, G. and Bona, L. C. E. (2012). A survey on cloud computing elasticity. In Proceedings of the International Workshop on Clouds and eScience Applications Management, CloudAM'12, pages 263-270. IEEE.

Galante, G. and Bona, L. C. E. (2014). Supporting elasticity in openmp applications. In Proceedings of the 2014 22nd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, PDP '14, page 188-195, USA. IEEE Computer Society.

Galante, G. and da Rosa Righi, R. (2017). Exploring cloud elasticity in scientific applications. In Antonopoulos, N. and Gillam, L., editors, Cloud Computing - Principles, Systems and Applications, Second Edition, Computer Communications and Networks, pages 101-125. Springer.

Galante, G. and Erpen De Bona, L. C. (2015). A programming-level approach for elasticizing parallel scientific applications. Journal of Systems and Software, 110:239-252.

Georgakoudis, G., Vandierendonck, H., Thoman, P., Supinski, B. R. D., Fahringer, T., and Nikolopoulos, D. S. (2017). Scalo: Scalability-aware parallelism orchestration for multi-threaded workloads. ACM Trans. Archit. Code Optim., 14(4).

Gordon, A. W. and Lu, P. (2011). Elastic phoenix: Malleable mapreduce for shared-memory systems. In Altman, E. R. and Shi,W., editors, Network and Parallel Computing - 8th IFIP International Conference, NPC 2011, volume 6985 of Lecture Notes in Computer Science, pages 1-16. Springer.

Grelck, C. (2015). Moldable applications on multi-core servers: Active resource management instead of passive resource administration. In Proceedings of the 18. Kolloquium Programmiersprachen und Grundlagen der Programmierung, KPS 2015, pages 1-10. TU Wien.

Grolemund, G. (2014). Hands-On Programming with R. O'Reilly Media, 1 edition.

Guo, X., Ipek, E., and Soyata, T. (2010). Resistive Computation: Avoiding the Power Wall with Low-Leakage, STT-MRAM Based Computing. In International Symposium on Computer Architecture (ISCA).

Gupta, A., Acun, B., Sarood, O., and Kalé, L. V. (2014). Towards realizing the potential of malleable jobs. In 2014 21st International Conference on High Performance Computing (HiPC), pages 1-10.

He, J., Wei, J., Chen, K., Tang, Z., Zhou, Y., and Zhang, Y. (2018). Multitier fog computing with large-scale iot data analytics for smart cities. IEEE Internet of Things Journal, 5(2):677-686.

Herbst, N. R., Kounev, S., and Reussner, R. (2013). Elasticity in cloud computing: What it is, and what it is not. In Proceedings of the 10th International Conference on Autonomic Computing, ICAC'13, pages 23-27. USENIX.

Herndon, T., Ash, M., and Pollin, R. (2014). Does high public debt consistently stifle economic growth? a critique of reinhart and rogoff. Cambridge Journal of Economics, 38(2):257-279.

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

HP Labs (2014). The Machine: A New Kind of Computer.

Huang, C., Lawlor, O., and Kalé, L. V. (2004). Adaptive mpi. In Rauchwerger, L., editor, Languages and Compilers for Parallel Computing, pages 306-322, Berlin, Heidelberg. Springer Berlin Heidelberg.

Hungershöfer, J. and Wierum, J. (2002). On the quality of partitions based on space-filling curves. In Sloot, P. M. A., Tan, C. J. K., Dongarra, J. J., and Hoekstra, A. G., editors, Computational Science - ICCS 2002, International Conference, Amsterdam, The Netherlands, April 21-24, 2002. Proceedings, Part III, volume 2331 of Lecture Notes in Computer Science, pages 36-45. Springer.

Hutson, S. (2010). Data handling errors spur debate over clinical trial. Nature Medicine, 16(6):618-618.

Intel (2018). Intel optane technology, press kit.

Intel (2021). eadr: New opportunities for persistent memory applications.

International Technology Roadmap for Semiconductors (ITRS) (2007). Emerging Research Devices.

International Technology Roadmap for Semiconductors (ITRS) (2009). Emerging Research Devices.

International Technology Roadmap for Semiconductors (ITRS) (2010). 2010 Update Overview.

Iserte, S. and Rojek, K. (2020). An study of the effect of process malleability in the energy efficiency on gpu-based clusters. J. Supercomput., 76(1):255-274.

Iserte, S., Mayo, R., Quintana-Ortí, E. S., Beltran, V., and PeËœna, A. J. (2018). Dmr api: Improving cluster productivity by turning applications into malleable. Parallel Computing, 78:54-66.

J. Dongarra, H. M. and Strohmaier, E. (2021). Top500 supercomputer: November 2021. https://www.top500.org/lists/2021/11/. [Accesed in: 3 Mar. 2022]. páginas

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

Jeongdong Choe, T. I. (2017). Intel 3d xpoint memory die removed from intel optane™ pcm (phase change memory).

Jiang, Y., Kodialam, M., Lakshman, T. V., Mukherjee, S., and Tassiulas, L. (2021). Resource allocation in data centers using fast reinforcement learning algorithms. IEEE Transactions on Network and Service Management.

Kalé, L. V., Kumar, S., and DeSouza, J. (2002). A malleable-job system for timeshared parallel machines. In Proceedings of the 2Nd IEEE/ACM International Symposium on Cluster Computing and the Grid, CCGRID '02, pages 230-, Washington, DC, USA. IEEE Computer Society.

Kale, V. (2020). Parallel computing architectures and APIs : IoT big data stream processing. CRC Press, Taylor & Francis Group, Boca Raton, FL.

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.

Kehrer, S. and Blochinger, W. (2020). Equilibrium: an elasticity controller for parallel tree search in the cloud. J. Supercomput., 76(11):9211-9245.

Kennedy, K., Mazina, M., Mellor-Crummey, J. M., Cooper, K. D., Torczon, L., Berman, F., Chien, A. A., Dail, H., Sievert, O., Angulo, D., Foster, I. T., Aydt, R. A., Reed, D. A., Gannon, D., Johnsson, S. L., Kesselman, C., Dongarra, J., Vadhiyar, S. S., and Wolski, R. (2002). Toward a framework for preparing and executing adaptive grid programs. In Proceedings of the 16th International Parallel and Distributed Processing Symposium, IPDPS '02, page 322, USA. IEEE Computer Society.

Kim, D., Larson, J. W., and Chiu, K. (2011). Toward malleable model coupling. Procedia Computer Science, 4:312-321. Proceedings of the International Conference on Computational Science, ICCS 2011.

Kim, E.-k., Shin, H., Jeon, B.-g., Han, S., Jung, J., and Won, Y. (2007). FRASH: hierarchical file system for FRAM and flash. In International Conference on Computational Science and Its Applications (ICCSA).

Klein, C. and Perez, C. (2011). An rms for non-predictably evolving applications. In 2011 IEEE International Conference on Cluster Computing, pages 326-334.

Klemm, M., Bezold, M., Gabriel, S., Veldema, R., and Philippsen, M. (2009). Reparallelization techniques for migrating openmp codes in computational grids. Concurrency and Computation: Practice and Experience, 21(3):281-299.

Lee, B. C., Ipek, E., Mutlu, O., and Burger, D. (2009). Architecting Phase Change Memory as a Scalable DRAM Architecture. In International Symposium on Computer Architecture (ISCA).

Lemarinier, P., Hasanov, K., Venugopal, S., and Katrinis, K. (2016). Architecting malleable mpi applications for priority-driven adaptive scheduling. In Proceedings of the 23rd European MPI Users' Group Meeting, EuroMPI 2016, page 74-81, New York, NY, USA. Association for Computing Machinery.

Leopold, C., Süß, M., and Breitbart, J. (2006). Programming for malleability with hybrid mpi-2 and openmp: Experiences with a simulation program for global water prognosis. In Proceedings of the European Conference on Modelling and Simulation, pages 665-670.

Libutti, L. A., Igual, F. D., Piñuel, L., De Giusti, L., and Naiouf, M. (2020). Towards a malleable tensorflow implementation. In Rucci, E., Naiouf, M., Chichizola, F., and De Giusti, L., editors, Cloud Computing, Big Data & Emerging Topics, pages 30-40, Cham. Springer International Publishing.

Lim, K., Chang, J., Mudge, T., Ranganathan, P., Reinhardt, S. K., and Wenisch, T. F. (2009). Disaggregated memory for expansion and sharing in blade servers. In Proceedings of the 36th Annual International Symposium on Computer Architecture, ISCA '09, pages 267-278, New York, NY, USA. ACM.

Liu, F. and Weissman, J. B. (2015). Elastic job bundling: An adaptive resource request strategy for large-scale parallel applications. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC '15, pages 1-12, New York, NY, USA. Association for Computing Machinery.

Müller, K. and Wickham, H. (2021). tibble: Simple Data Frames. R package version 3.1.6.

Martín, G., Singh, D. E., Marinescu, M.-C., and Carretero, J. (2015). Enhancing the performance of malleable mpi applications by using performance-aware dynamic reconfiguration. Parallel Comput., 46(C):60-77.

Mascagni, M., Iserte, S., Martínez, H., Barrachina, S., Castillo, M., Mayo, R., and PeËœna, A. J. (2019). Dynamic reconfiguration of noniterative scientific applications: A case study with hpg aligner. Int. J. High Perform. Comput. Appl., 33(5):804-816.

Masuoka, F., Asano, M., Iwahashi, H., Komuro, T., and Tanaka, S. (1984). A new flash e2prom cell using triple polysilicon technology. In Electron Devices Meeting, 1984 International, volume 30, pages 464-467.

Mayes, K., Luján, M., Riley, G., Chin, J., Coveney, P., and Gurd, J. (2005). Towards performance control on the grid. Phil. Trans. R. Soc., 363(1833):1793-1805.

McFarland, D. J. (2011). Exploiting malleable parallelism on multicore systems. Master's thesis, Faculty of the Virginia Polytechnic Institute and State University.

McGrath, D. (2008). 'universal memory' race still on the starting block. EE Times.

Micron (2006). 1Gb: x4, x8, x16 DDR3 SDRAM Features.

Micron (2006). TN-29-19: NAND Flash 101 NAND vs. NOR Comparison.

Mishra, A. K., Dong, X., Sun, G., Xie, Y., Vijaykrishnan, N., and Das, C. R. (2011). Architecting on-chip interconnects for stacked 3D STT-RAM caches in CMPs. In International Symposium on Computer Architecture (ISCA).

Mo-Hellenbrand, A., Comprés, I., Meister, O., Bungartz, H.-J., Gerndt, M., and Bader, M. (2017). A large-scale malleable tsunami simulation realized on an elastic mpi infrastructure. In Proceedings of the Computing Frontiers Conference, CF'17, page 271-274, New York, NY, USA. Association for Computing Machinery.

Mohan, C., Haderle, D., Lindsay, B., Pirahesh, H., and Schwarz, P. (1992). ARIES: A Transaction Recovery Method Supporting Fine-Granularity Locking and Partial Rollbacks Using Write-Ahead Logging. ACM Trans. Database Syst., 17(1):94-162.

Naha, R. K., Garg, S., Chan, A., and Battula, S. K. (2020). Deadlinebased dynamic resource allocation and provisioning algorithms in fog-cloud environment. Future Generation Computer Systems, 104:131 - 141.

Nguyen, N. D., Phan, L. A., Park, D. H., Kim, S., and Kim, T. (2020). Elasticfog: Elastic resource provisioning in container-based fog computing. IEEE Access, 8:183879-183890.

Pagani, D. H., Bona, L. C. E. D., and Galante, G. (2016). Uma abordagem baseada em níveis de estresse para alocação elástica de recursos em sistema de bancos de dados. In Anais do XIV Workshop em Clouds e Aplicações, WCGA 2016, pages 1-14, Porto Alegre. SBC.

Pedersen, T. L. (2020). patchwork: The Composer of Plots. R package version 1.1.1.

Peng, I., Wu, K., Ren, J., Li, D., and Gokhale, M. (2020). Demystifying the Performance of HPC Scientific Applications on NVM-based Memory Systems. In 2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS), pages 916-925.

Pinto, V. G., Nesi, L. L., and Schnorr, L. M. (2020). Boas Práticas para Experimentos Computacionais de Alto Desempenho. In du Bois, A. R. and Castro, M. B., editors, Minicursos da XX Escola Regional de Alto Desempenho da Região Sul, pages 1-19. Sociedade Brasileira de Computação - SBC.

Prabhakaran, S., Iqbal, M., Rinke, S., Windisch, C., and Wolf, F. (2014). A batch system with fair scheduling for evolving applications. In Proceedings of the 2014 Brazilian Conference on Intelligent Systems, BRACIS '14, page 351-360, USA. IEEE Computer Society.

Qureshi, M. K., Srinivasan, V., and Rivers, J. A. (2009). Scalable High Performance Main Memory System Using Phase-Change Memory Technology. In International Symposium on Computer Architecture (ISCA).

R Core Team (2021). R Language Definition.

Rajan, D. and Thain, D. (2017). Designing self-tuning splitmap-merge applications for high cost-efficiency in the cloud. IEEE Transactions on Cloud Computing, 5(2):303-316.

Ramos, L. and Bianchini, R. (2012). Exploiting phasechange memory in cooperative caches. In Computer Architecture and High Performance Computing (SBAC-PAD), 2012 IEEE 24th International Symposium on, pages 227-234.

Ramos, L. E., Gorbatov, E., and Bianchini, R. (2011). Page placement in hybrid memory systems. In Proceedings of the International Conference on Supercomputing, ICS '11, pages 85-95, New York, NY, USA. ACM.

Raveendran, A., Bicer, T., and Agrawal, G. (2011). A framework for elastic execution of existing mpi programs. In 2011 IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum, pages 940-947.

Ribeiro, F., Rebello, V., Nascimento, A., Boeres, C., and Sena, A. (2013). Autonomic malleability in iterative mpi applications. In Proceedings of the 2013 25th International Symposium on Computer Architecture and High Performance Computing, SBAC-PAD '13, page 192-199, USA. IEEE Computer Society.

Rodrigues, V. F., da Rosa Righi, R., da Costa, C. A., Singh, D., MuËœnoz, V. M., and Chang, V. (2018). Towards combining reactive and proactive cloud elasticity on running HPC applications. In Muñoz, V. M., Wills, G. B., Walters, R. J., Firouzi, F., and Chang, V., editors, Proceedings of the 3rd International Conference on Internet of Things, Big Data and Security, IoTBDS 2018, pages 261-268. SciTePress.

Rodrigues, V. F., da Rosa Righi, R., Rostirolla, G., Barbosa, J. L. V., da Costa, C. A., Alberti, A. M., and Chang, V. I. (2017). Towards enabling live thresholding as utility to manage elastic master-slave applications in the cloud. J. Grid Comput., 15(4):535-556.

Scargall, S. (2020). Programming Persistent Memory - A Comprehensive Guide for Developers. Apress, 1st edition.

Schnorr, L. M. and Pinto, V. G. (2019). Boas Práticas para Experimentos. In Anais da XIX Escola Regional de Alto Desempenho da Região Sul, pages 45-64. Sociedade Brasileira de Computação - SBC.

Sengupta, A. (2019). Julia High Performance Optimizations, distributed computing, multithreading, and GPU programming with Julia 1.0 and beyond. Packt Publishing. páginas

Serpa, M. S., Moreira, F. B., Navaux, P. O., Cruz, E. H., Diener, M., Griebler, D., and Fernandes, L. G. (2019). Memory performance and bottlenecks in multicore and gpu architectures. In 2019 27th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP), pages 233-236. IEEE. páginas

Serpa, M. S., Pavan, P. J., Cruz, E. H., Machado, R. L., Panetta, J., Azambuja, A., Carissimi, A. S., and Navaux, P. O. (2021). Energy efficiency and portability of oil and gas simulations on multicore and graphics processing unit architectures. Concurrency and Computation: Practice and Experience, 33(18):e6212. páginas

Sievert, C. (2020). Interactive Web-Based Data Visualization with R, plotly, and shiny. Chapman and Hall/CRC.

Small, N., Akkermans, S., Joosen, W., and Hughes, D. (2017). Niflheim: An end-to-end middleware for applications on a multi-tier iot infrastructure. In 2017 IEEE 16th International Symposium on Network Computing and Applications (NCA), pages 1-8.

Solihin, Y. (2019). Persistent Memory: Abstractions, Abstractions, and Abstractions. IEEE Micro, 39(1):65-66.

Spenke, F., Balzer, K., Frick, S., Hartke, B., and Dieterich, J. M. (2019). Malleable parallelism with minimal effort for maximal throughput and maximal hardware load. Computational and Theoretical Chemistry, 1151:72-77.

Stallings, W. (2017.). Computer organization and architecture. Pearson Education, Inc., Hoboken, New Jersey, 10th ed. edition.

Sudarsan, R. and Ribbens, C. J. (2007). Reshape: A framework for dynamic resizing and scheduling of homogeneous applications in a parallel environment. In Proceedings of the 2007 International Conference on Parallel Processing, ICPP '07, page 44, USA. IEEE Computer Society.

Sudarsan, R., Ribbens, C. J., and Farkas, D. (2009). Dynamic resizing of parallel scientific simulations: A case study using lammps. In Proceedings of the 9th International Conference on Computational Science: Part I, ICCS '09, page 175-184, Berlin, Heidelberg. Springer-Verlag.

Suleman, M. A., Qureshi, M. K., and Patt, Y. N. (2008). Feedbackdriven threading: Power-efficient and high-performance execution of multi-threaded workloads on cmps. SIGARCH Comput. Archit. News, 36(1):277-286.

Toshiba (2006). NAND vs. NOR Flash memory.

Tyson, M. (2019). Intel Optane DC Persistent Memory launched. Retrieved from https://hexus.net/tech/news/storage/129143-intel-optane-dc-persistent-memory-launched/.

Utrera, G., Corbalan, J., and Labarta, J. (2004). Implementing malleability on mpi jobs. In Proceedings of the 13th International Conference on Parallel Architectures and Compilation Techniques, PACT '04, page 215-224, USA. IEEE Computer Society.

Vadhiyar, S. S. and Dongarra, J. J. (2003). Srs: A framework for developing malleable and migratable parallel applications for distributed systems. Parallel Processing Letters, 13(02):291-312.

Van Nieuwpoort, R. V., Wrzesinska, G., Jacobs, C. J. H., and Bal, H. E. (2010). Satin: A high-level and efficient grid programming model. ACM Trans. Program. Lang. Syst., 32(3).

Vance, A. (2014). With 'the machine,' HP may have invented a new kind of computer. BusinessWeek: technology.

Venables, W. N., Smith, D. M., and R Core Team (2021). An Introduction to R. Technical report.

Wickham, H. (2010). A layered grammar of graphics. Journal of Computational and Graphical Statistics, 19(1):3-28.

Wickham, H. (2014). Tidy data. The Journal of Statistical Software, 59.

Wickham, H. (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York.

Wickham, H. and Girlich, M. (2022). tidyr: Tidy Messy Data. R package version 1.2.0.

Wickham, H. and Hester, J. (2021). readr: Read Rectangular Text Data. R package version 2.0.1.

Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L. D., François, R., Grolemund, G., Hayes, A., Henry, L., Hester, J., Kuhn, M., Pedersen, T. L., Miller, E., Bache, S. M., Müller, K., Ooms, J., Robinson, D., Seidel, D. P., Spinu, V., Takahashi, K., Vaughan, D., Wilke, C., Woo, K., and Yutani, H. (2019). Welcome to the tidyverse. Journal of Open Source Software, 4(43):1686.

Wickham, H., François, R., Henry, L., and Müller, K. (2021). dplyr: A Grammar of Data Manipulation. R package version 1.0.7.

Wilkinson, B. (2009). Grid Computing: Techniques and Applications. CRC Press, Boca Raton, FL, 1st ed. edition.

Wilkinson, L. (2012). The grammar of graphics. In Handbook of computational statistics, pages 375-414. Springer.

Wrzesinska, G., van Nieuwpoort, R., Maassen, J., and Bal, H. (2005). Fault-tolerance, malleability and migration for divide-and-conquer applications on the grid. In 19th IEEE International Parallel and Distributed Processing Symposium, pages 10 pp.-.

Wu, X., Li, J., Zhang, L., Speight, E., Rajamony, R., and Xie, Y. (2009). Hybrid Cache Architecture with Disparate Memory Technologies. In International Symposium on Computer Architecture (ISCA).

Yadav, M. P., Rohit, and Yadav, D. K. (2021). Resource provisioning through machine learning in cloud services. Arabian Journal for Science and Engineering.

Yin, L., Luo, J., and Luo, H. (2018). Tasks scheduling and resource allocation in fog computing based on containers for smart manufacturing. IEEE Transactions on Industrial Informatics, 14(10):4712-4721.

Yoo, A. B., Jette, M. A., and Grondona, M. (2003). Slurm: Simple linux utility for resource management. In Workshop on Job Scheduling Strategies for Parallel Processing, pages 44-60. Springer. páginas

Zhang,W. and Li, T. (2009). Exploring Phase Change Memory and 3D Die-Stacking for Power/Thermal Friendly, Fast and Durable Memory Architectures. In International Conference on Parallel Architectures and Compilation Techniques (PACT).

Zhou, P., Zhao, B., Yang, J., and Zhang, Y. (2009). A Durable and Energy Efficient Main Memory Using Phase Change Memory Technology. In International Symposium on Computer Architecture (ISCA).