Pub Date : 2022-04-07DOI: 10.5753/eradsp.2022.222246
Francisco Wallison Rocha, E. Francesquini, Daniel Cordeiro
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.
{"title":"Fast SimEDaPE: Simulation Estimation by Data Patterns Exploration","authors":"Francisco Wallison Rocha, E. Francesquini, Daniel Cordeiro","doi":"10.5753/eradsp.2022.222246","DOIUrl":"https://doi.org/10.5753/eradsp.2022.222246","url":null,"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.","PeriodicalId":251067,"journal":{"name":"Anais da XIII Escola Regional de Alto Desempenho de São Paulo (ERAD-SP 2022)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133302243","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：