Otimização do Desempenho em Memórias Transacionais com Aprendizado de Máquina
Resumo
Memórias Transacionais reduzem a possibilidade de erros na programação e a ocorrência de deadlocks durante a execução em sistemas com múltiplas threads, entretanto, o desempenho geral está relacionado à escolha de políticas e parâmetros de configuração. Neste trabalho são propostas melhorias no controle de paralelismo na Memória Transacional e mapeamento de threads, usando-se Aprendizado de Máquina.Referências
Castro, M., Góes, L., and Méhaut, J.-F. (2014). Adaptive thread mapping strategies for transactional memory applications. Journal of Parallel and Distributed Computing, 74.
Di Sanzo, P., Pellegrini, A., Sannicandro, M., Ciciani, B., and Quaglia, F. (2019). Adaptive model-based scheduling in software transactional memory. IEEE Transactions on Computers, PP:1–1.
Didona, D., Diegues, N., Guerraoui, R., Kermarrec, A.-M., Neves, R., and Romano, P. (2016). ProteusTM: Abstraction Meets Performance in Transactional Memory. In Twenty First International Conference on Architectural Support for Programming Languages and Operating Systems, Atlanta, United States.
Frank, J. and Chun, R. (2008). Adaptive software transactional memory: A dynamic approach to contention management. pages 40–46.
Pasqualin, D. P., Diener, M., Du Bois, A. R., and Pilla, M. L. (2020). Thread affinity in software transactional memory. In 2020 19th International Symposium on Parallel and Distributed Computing (ISPDC), pages 180–187.
Rughetti, D., Di Sanzo, P., Ciciani, B., and Quaglia, F. (2012). Machine learning-based self-adjusting concurrency in software transactional memory systems. In 2012 IEEE 20th International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems, pages 278–285.
Wang, Q., Kulkarni, S., Cavazos, J., and Spear, M. (2012). A transactional memory with automatic performance tuning. TACO, 8:54.
Di Sanzo, P., Pellegrini, A., Sannicandro, M., Ciciani, B., and Quaglia, F. (2019). Adaptive model-based scheduling in software transactional memory. IEEE Transactions on Computers, PP:1–1.
Didona, D., Diegues, N., Guerraoui, R., Kermarrec, A.-M., Neves, R., and Romano, P. (2016). ProteusTM: Abstraction Meets Performance in Transactional Memory. In Twenty First International Conference on Architectural Support for Programming Languages and Operating Systems, Atlanta, United States.
Frank, J. and Chun, R. (2008). Adaptive software transactional memory: A dynamic approach to contention management. pages 40–46.
Pasqualin, D. P., Diener, M., Du Bois, A. R., and Pilla, M. L. (2020). Thread affinity in software transactional memory. In 2020 19th International Symposium on Parallel and Distributed Computing (ISPDC), pages 180–187.
Rughetti, D., Di Sanzo, P., Ciciani, B., and Quaglia, F. (2012). Machine learning-based self-adjusting concurrency in software transactional memory systems. In 2012 IEEE 20th International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems, pages 278–285.
Wang, Q., Kulkarni, S., Cavazos, J., and Spear, M. (2012). A transactional memory with automatic performance tuning. TACO, 8:54.
Publicado
18/04/2022
Como Citar
PERLIN, Tiago; DU BOIS, Andre Rauber.
Otimização do Desempenho em Memórias Transacionais com Aprendizado de Máquina. In: ESCOLA REGIONAL DE ALTO DESEMPENHO DA REGIÃO SUL (ERAD-RS), 22. , 2022, Curitiba.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 75-76.
ISSN 2595-4164.
DOI: https://doi.org/10.5753/eradrs.2022.19169.
