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Uso de GPUs en aplicaciones de tiempo real: Una revisión de técnicas para el análisis y optimización de parámetros temporales

  • Gomez, Iosu [2] ; Díaz de Cerio, Unai [2] ; Parra, Jorge [2] ; Rivas, Juan M. [1] ; Gutiérrez, J. Javier [1] Árbol académico
    1. [1] Universidad de Cantabria

      Universidad de Cantabria

      Santander, España

    2. [2] Ikerlan
  • Localización: Revista iberoamericana de automática e informática industrial ( RIAI ), ISSN-e 1697-7920, Vol. 21, Nº. 1, 2024, págs. 1-16
  • Idioma: español
  • DOI: 10.4995/riai.2023.20321
  • Títulos paralelos:
    • Using GPUs in Real-Time Applications: A Review of Techniques for Analyzing and Optimizing the Timing Parameters
  • Enlaces
  • Resumen
    • español

      La conducción autónoma despierta un interés cada vez mayor en la industria, no solo en el sector de la automoción, sino también en el transporte de personas o mercancías por carretera o ferrocarril y en entornos de fabricación más controlados. Los sistemas ciber-físicos que se están proponiendo para este tipo de aplicaciones requieren de una gran capacidad de cómputo (arquitecturas hardware con varios núcleos, GPUs, NPUs…) para poder atender y reaccionar a una múltiple y compleja cantidad de sensores (cámaras, radar, LiDAR, medida de distancia, etc.). Por otro lado, este tipo de sistemas debe atender a requisitos de seguridad funcional y también de tiempo real. Este último aspecto plantea retos en los que se está trabajando intensamente y en los que aún quedan muchas cuestiones por resolver. En este trabajo, se hace una revisión de la literatura más reciente del uso de arquitecturas heterogéneas con GPUs en aplicaciones de tiempo real. Estos trabajos proponen soluciones para la estimación de cotas de tiempos de ejecución y respuesta temporal, proponiendo diferentes estrategias de optimización destacando la mitigación de interferencia en la memoria.

    • English

      Autonomous driving is attracting an increasing attention in industry, not only in the automotive sector, but also in the transport of people or goods by road or railway and in more controlled manufacturing environments. The cyber-physical systems that are being proposed for this type of applications require a large computing capacity (hardware architectures with several cores, GPUs, NPUs...) to be able to attend and react to a multiple and complex amount of sensors (cameras, radar, LiDAR, measure of distance, etc.). On the other hand, this type of system must meet both safety and real-time requirements. This last aspect poses challenges on which intensive work is being done and on which there are still many open issues. In this work, a review of the most recent literature on the use of heterogeneous architectures with GPUs in real-time applications is made. These works mainly propose some solutions to the estimation of bounds to the execution times and response times, and consider different optimization strategies emphasising memory interference mitigation.

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