Fast-Tracking Scalability Analysis: The PaScal Paramount Approach
Resumo
Scalability analysis of High-Performance Computing applications is essential, but its process is time-consuming and costly. This work introduces PaScal Paramount, a new feature of the PaScal Analyzer tool that accelerates this analysis by estimating the total performance from the execution of a small fraction of the parallel code. Experiments demonstrated that the approach reduces analysis time by up to 89% while maintaining low estimation error and generating performance patterns faithful to the full execution. The feature offers an adjustable trade-off between speed and accuracy, proving to be an effective solution for gaining rapid insights into the behavior of applications, thereby guiding their optimization process.Referências
Adve, V. and Sakellariou, R. (2000). Application representations for multiparadigm performance modeling of large-scale parallel scientific codes. The International Journal of High Performance Computing Applications, 14(4).
Cameron, K. W., Ge, R., and Feng, X. (2005). High-performance, power-aware distributed computing for scientific applications. Computer, 38(11):40–47.
Chen, J., Becker, B. A., Ouyang, Y., and Shen, L. (2021). What Influences Students’ Understanding of Scalability Issues in Parallel Computing. The Journal of Computational Science Education, 12(2):58–65.
da Silva, A. B., Cunha, D. A., Silva, V. R., de A. Furtunato, A. F., and Xavier-de Souza, S. (2019). PaScal Viewer: A Tool for the Visualization of Parallel Scalability Trends. In International Workshop on Extreme-Scale Programming Tools, pages 250–264. Springer.
da Silva, V., da Silva, A., Valderrama, C., Manneback, P., and Xavier-de Souza, S. (2022). A Minimally Intrusive Approach for Automatic Assessment of Parallel Performance Scalability of Shared-Memory HPC Applications. Electronics, 11:689.
Gomes, J. F. P. (2025). Extending the PaScal Analyzer for MPI Scalability Analysis: Design, Implementation, and Validation. B.s. thesis, Universidade Federal do Rio Grande do Norte.
Kumar, V. P. and Gupta, A. (1994). Analyzing scalability of parallel algorithms and architectures. Journal of parallel and distributed computing, 22(3):379–391.
Ramachandran, U., Venkateswaran, H., Sivasubramaniam, A., and Singla, A. (1994). Issues in understanding the scalability of parallel systems. In Proceedings of the First International Workshop on Parallel Processing, Bangalore, India, pages 399–404.
Tavares, W., Reis, L., Brunetta, J., and Borin, E. (2019). Aplicação da técnica Paramount Iteration nas aplicações BLAST e DNN-ROM na nuvem computacional. In Anais do XX Simpósio em Sistemas Computacionais de Alto Desempenho, pages 228–239. SBC.
Yang, L. T., Ma, X., and Mueller, F. (2005). Cross-Platform Performance Prediction of Parallel Applications Using Partial Execution. IEEE.
Zhang, M., Hao, M., and Snir, M. (2017). Predicting HPC parallel program performance based on LLVM compiler. Cluster Computing, 20(2):1233–1244.
Cameron, K. W., Ge, R., and Feng, X. (2005). High-performance, power-aware distributed computing for scientific applications. Computer, 38(11):40–47.
Chen, J., Becker, B. A., Ouyang, Y., and Shen, L. (2021). What Influences Students’ Understanding of Scalability Issues in Parallel Computing. The Journal of Computational Science Education, 12(2):58–65.
da Silva, A. B., Cunha, D. A., Silva, V. R., de A. Furtunato, A. F., and Xavier-de Souza, S. (2019). PaScal Viewer: A Tool for the Visualization of Parallel Scalability Trends. In International Workshop on Extreme-Scale Programming Tools, pages 250–264. Springer.
da Silva, V., da Silva, A., Valderrama, C., Manneback, P., and Xavier-de Souza, S. (2022). A Minimally Intrusive Approach for Automatic Assessment of Parallel Performance Scalability of Shared-Memory HPC Applications. Electronics, 11:689.
Gomes, J. F. P. (2025). Extending the PaScal Analyzer for MPI Scalability Analysis: Design, Implementation, and Validation. B.s. thesis, Universidade Federal do Rio Grande do Norte.
Kumar, V. P. and Gupta, A. (1994). Analyzing scalability of parallel algorithms and architectures. Journal of parallel and distributed computing, 22(3):379–391.
Ramachandran, U., Venkateswaran, H., Sivasubramaniam, A., and Singla, A. (1994). Issues in understanding the scalability of parallel systems. In Proceedings of the First International Workshop on Parallel Processing, Bangalore, India, pages 399–404.
Tavares, W., Reis, L., Brunetta, J., and Borin, E. (2019). Aplicação da técnica Paramount Iteration nas aplicações BLAST e DNN-ROM na nuvem computacional. In Anais do XX Simpósio em Sistemas Computacionais de Alto Desempenho, pages 228–239. SBC.
Yang, L. T., Ma, X., and Mueller, F. (2005). Cross-Platform Performance Prediction of Parallel Applications Using Partial Execution. IEEE.
Zhang, M., Hao, M., and Snir, M. (2017). Predicting HPC parallel program performance based on LLVM compiler. Cluster Computing, 20(2):1233–1244.
Publicado
28/10/2025
Como Citar
MAIA, Reilta Christine Dantas; XAVIER-DE-SOUZA, Samuel.
Fast-Tracking Scalability Analysis: The PaScal Paramount Approach. In: SIMPÓSIO EM SISTEMAS COMPUTACIONAIS DE ALTO DESEMPENHO (SSCAD), 26. , 2025, Bonito/MS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 374-385.
DOI: https://doi.org/10.5753/sscad.2025.16734.
