Extensão da Ferramenta MultiExplorer para Exploração de Projetos de GPUs e Máquinas Virtuais
Resumo
Este trabalho apresenta o projeto e desenvolvimento da ferramenta MultiExplorer, que oferece suporte à exploração de múltiplos fluxos de projetos de arquiteturas de computadores. Os fluxos de projeto desenvolvidos neste trabalho (GPUs e máquinas virtuais) abrangem desde a simulação funcional, a exploração física dos recursos computacionais e a exploração do espaço de projeto.Referências
Amorim, E. and Duenha, L. (2023). Uma ferramenta para ensino e aprendizado de exploração de espaço de projeto de arquiteturas de processadores na era de dark-silicon. Master’s thesis, Universidade Federal de Mato Grosso do Sul.
Arigoni, D. C. A., dos Santos, R. R., and Garanhani, L. D. D. (2022). Design space exploration of heterogeneous systems applied to the cloud resource allocation problem. In Proceedings of the SSCAD. SBC.
Carlson, T. E., Heirman, W., and Eeckhout, L. (2011). Sniper: exploring the level of abstraction for scalable and accurate parallel multi-core simulation. In Proceedings of 2011 SC Conference, New York, NY, USA. Association for Computing Machinery.
Deb, K., Pratap, A., Agarwal, S., and Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE Transactions on Evolutionary Computation, 6(2):182–197.
Devigo, R., Duenha, L., Azevedo, R., and Santos, R. (2015). Multiexplorer: A tool set for multicore system-on-chip design exploration. In Proceedings of the ASAP Conference, pages 160–161.
Duenha, L., Guedes, M., Almeida, H., Boy, M., and Azevedo, R. (2014). Mpsocbench: A toolset for mpsoc system level evaluation. In Proceedings of the SAMOS XIV Conference, pages 164–171.
Goyal, T., Singh, A., and Agrawal, A. (2012). Cloudsim: simulator for cloud computing infrastructure and modeling. Procedia Engineering, 38:3566–3572.
Kang, E., Jackson, E., and Schulte, W. (2010). An approach for effective design space exploration. In Proceedings of the 16th FOCS, page 33–54, Berlin, Heidelberg. Springer-Verlag.
Khairy, M., Shen, Z., Aamodt, T. M., and Rogers, T. G. (2020). Accel-sim: An extensible simulation framework for validated gpu modeling. In Proceedings of the ISCA Conference, pages 473–486.
Kinsy, M. A., Pellauer, M., and Devadas, S. (2013). Heracles: a tool for fast rtl-based design space exploration of multicore processors. In Proceedings of the FPGA Conference, page 125–134, New York, NY, USA. Association for Computing Machinery.
Leng, J., Hetherington, T., ElTantawy, A., Gilani, S., Kim, N. S., Aamodt, T. M., and Reddi, V. J. (2013). Gpuwattch: enabling energy optimizations in gpgpus. SIGARCH Computing Architecture News, 41(3):487–498.
Li, S., Ahn, J. H., Strong, R. D., Brockman, J. B., Tullsen, D. M., and Jouppi, N. P. (2009). Mcpat: An integrated power, area, and timing modeling framework for multicore and manycore architectures. In Proceedings of the MICRO Conference, pages 469–480.
Mei, L., Houshmand, P., Jain, V., Giraldo, S., and Verhelst, M. (2021). Zigzag: Enlarging joint architecture-mapping design space exploration for dnn accelerators. IEEE Transactions on Computers, 70(8):1160–1174.
Paraskevopoulos, N., Hamel, D., Sarkar, A., Almudever, C. G., and Feld, S. (2024). Arta: Automating design space exploration of spin qubit architectures.
Santos, M. T., Duenha, L., Magalhaes, F. C., and Santos, R. (2017). Avaliação de preditores de desvios por meio de simuladores como parte do processo de ensino e aprendizagem de arquitetura de computadores. In IJCAE.
Silva, A. C., Bignardi, T., de Palma, E., Alves, R., Hayashi, C., and Santos, R. (2015). Identificação automática de dark silicon em processadores multicore. In Proceedings of the SSCAD. SBC.
Sonohata, R., Arigoni, D. C. A., Fernandes, E. R., dos Santos, R. R., and Duenha, L. D. (2023). Performance predictors for graphics processing units applied to dark-silicon-aware design space exploration. Concurrency and Computation, 35(17).
Arigoni, D. C. A., dos Santos, R. R., and Garanhani, L. D. D. (2022). Design space exploration of heterogeneous systems applied to the cloud resource allocation problem. In Proceedings of the SSCAD. SBC.
Carlson, T. E., Heirman, W., and Eeckhout, L. (2011). Sniper: exploring the level of abstraction for scalable and accurate parallel multi-core simulation. In Proceedings of 2011 SC Conference, New York, NY, USA. Association for Computing Machinery.
Deb, K., Pratap, A., Agarwal, S., and Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE Transactions on Evolutionary Computation, 6(2):182–197.
Devigo, R., Duenha, L., Azevedo, R., and Santos, R. (2015). Multiexplorer: A tool set for multicore system-on-chip design exploration. In Proceedings of the ASAP Conference, pages 160–161.
Duenha, L., Guedes, M., Almeida, H., Boy, M., and Azevedo, R. (2014). Mpsocbench: A toolset for mpsoc system level evaluation. In Proceedings of the SAMOS XIV Conference, pages 164–171.
Goyal, T., Singh, A., and Agrawal, A. (2012). Cloudsim: simulator for cloud computing infrastructure and modeling. Procedia Engineering, 38:3566–3572.
Kang, E., Jackson, E., and Schulte, W. (2010). An approach for effective design space exploration. In Proceedings of the 16th FOCS, page 33–54, Berlin, Heidelberg. Springer-Verlag.
Khairy, M., Shen, Z., Aamodt, T. M., and Rogers, T. G. (2020). Accel-sim: An extensible simulation framework for validated gpu modeling. In Proceedings of the ISCA Conference, pages 473–486.
Kinsy, M. A., Pellauer, M., and Devadas, S. (2013). Heracles: a tool for fast rtl-based design space exploration of multicore processors. In Proceedings of the FPGA Conference, page 125–134, New York, NY, USA. Association for Computing Machinery.
Leng, J., Hetherington, T., ElTantawy, A., Gilani, S., Kim, N. S., Aamodt, T. M., and Reddi, V. J. (2013). Gpuwattch: enabling energy optimizations in gpgpus. SIGARCH Computing Architecture News, 41(3):487–498.
Li, S., Ahn, J. H., Strong, R. D., Brockman, J. B., Tullsen, D. M., and Jouppi, N. P. (2009). Mcpat: An integrated power, area, and timing modeling framework for multicore and manycore architectures. In Proceedings of the MICRO Conference, pages 469–480.
Mei, L., Houshmand, P., Jain, V., Giraldo, S., and Verhelst, M. (2021). Zigzag: Enlarging joint architecture-mapping design space exploration for dnn accelerators. IEEE Transactions on Computers, 70(8):1160–1174.
Paraskevopoulos, N., Hamel, D., Sarkar, A., Almudever, C. G., and Feld, S. (2024). Arta: Automating design space exploration of spin qubit architectures.
Santos, M. T., Duenha, L., Magalhaes, F. C., and Santos, R. (2017). Avaliação de preditores de desvios por meio de simuladores como parte do processo de ensino e aprendizagem de arquitetura de computadores. In IJCAE.
Silva, A. C., Bignardi, T., de Palma, E., Alves, R., Hayashi, C., and Santos, R. (2015). Identificação automática de dark silicon em processadores multicore. In Proceedings of the SSCAD. SBC.
Sonohata, R., Arigoni, D. C. A., Fernandes, E. R., dos Santos, R. R., and Duenha, L. D. (2023). Performance predictors for graphics processing units applied to dark-silicon-aware design space exploration. Concurrency and Computation, 35(17).
Publicado
23/10/2024
Como Citar
RODRIGUES, Samuel; SANTOS, Ricardo.
Extensão da Ferramenta MultiExplorer para Exploração de Projetos de GPUs e Máquinas Virtuais. In: WORKSHOP DE INICIAÇÃO CIENTÍFICA - SIMPÓSIO EM SISTEMAS COMPUTACIONAIS DE ALTO DESEMPENHO (SSCAD), 25. , 2024, São Carlos/SP.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 33-40.
DOI: https://doi.org/10.5753/sscad_estendido.2024.244733.
