Fast SimEDaPE: Simulation Estimation by Data Patterns Exploration

• Francisco Wallison Rocha USP http://orcid.org/0000-0001-7370-8278
• Emilio Francesquini UFABC https://orcid.org/0000-0002-5374-2521
• Daniel Cordeiro USP https://orcid.org/0000-0003-4971-7355

DOI: https://doi.org/10.5753/eradsp.2022.222246

Abstract

In the context of smart cities, solving problems such as pollution, congestion, and public transport, regularly faced by large cities like São Paulo, is not trivial. To tackle those problems researchers often rely on simulations. An example of a smart city simulator is InterSCSimulator, which simulates urban traffic. However, this simulator has limitations regarding its performance in large scale scenarios. SimEDaPE, a technique used to improve simulation performance based on the recurrence of patterns from previous simulations, was proposed in this context. SimEDaPE is still under active development and as such has some performance bottlenecks in some stages, such as the temporal mapping stage. In this work, we propose an improvement to this step of SimEDaPE using optimized libraries (written in C instead of Python), and parallelism. As a result, we obtained a considerable relative performance of 156x, running on 8 cores compared to the reference sequential implementation.

Keywords: Parallel and Distributed Algorithms, HPC Applications

References

Berndt, D. J. and Clifford, J. (1994). Using dynamic time warping to find patterns in time series. In Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining, AAAIWS'94, page 359-370, Seattle, WA. AAAI Press.

Hamerly, G., Perelman, E., Lau, J., and Calder, B. (2005). Simpoint 3.0: Faster and more flexible program phase analysis. Journal of Instruction Level Parallelism, 7(4):1-28.

Paparrizos, J. and Gravano, L. (2015). K-shape: Efficient and accurate clustering of time series. In Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, SIGMOD '15, page 1855-1870, New York, NY, USA. Association for Computing Machinery.

Rocha, F. W., Fukuda, J. C., Francesquini, E., and Cordeiro, D. (2021). Accelerating smart city simulations. Latin America High Performance Computing Conference. To publish.

Santana, E. F. Z., Lago, N., Kon, F., and Milojicic, D. S. (2017). InterSCSimulator: Large-scale traffic simulation in smart cities using erlang. In International Workshop on Multi-Agent Systems and Agent-Based Simulation, pages 211-227. Springer.