Performance scalability analysis of JavaScript applications with web workers
Web applications are getting closer to the performance of native applications taking advantage of new standard–based technologies. The recent HTML5 standard includes, among others, the Web Workers API that allows executing JavaScript applications on multiple threads, or workers. However, the interna...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2016 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/99263 |
| Acceso en línea: | https://hdl.handle.net/2117/99263 https://dx.doi.org/10.1109/LCA.2015.2494585 |
| Access Level: | acceso abierto |
| Palabra clave: | Web applications HTML5 Web workers JavaScript Web apps Parallelism Multithreading Aplicacions web Àrees temàtiques de la UPC::Informàtica::Arquitectura de computadors |
| Sumario: | Web applications are getting closer to the performance of native applications taking advantage of new standard–based technologies. The recent HTML5 standard includes, among others, the Web Workers API that allows executing JavaScript applications on multiple threads, or workers. However, the internals of the browser’s JavaScript virtual machine does not expose direct relation between workers and running threads in the browser and the utilization of logical cores in the processor. As a result, developers do not know how performance actually scales on different environments and therefore what is the optimal number of workers on parallel JavaScript codes. This paper presents the first performance scalability analysis of parallel web apps with multiple workers. We focus on two case studies representative of different worker execution models. Our analyses show performance scaling on different parallel processor microarchitectures and on three major web browsers in the market. Besides, we study the impact of co–running applications on the web app performance. The results provide insights for future approaches to automatically find out the optimal number of workers that provide the best tradeoff between performance and resource usage to preserve system responsiveness and user experience, especially on environments with unexpected changes on system workload. |
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