Dynamic schedule management framework for aperiodic soft-real-time jobs on GPU based architectures
Kiriti Nagesh Gowda, Harini Ramaprasad · 2020
The Graphics Processing Unit (GPU) was originally designed for the rapid creation and manipulation of images. Since then, it has evolved from being just an application-specific processing unit to supporting more general-purpose computing (GPGPU). GPU based architectures are optimized for throughput and performance per watt, which provides huge computational gains at a fraction of the power when compared to traditional CPU based architectures. As real-time systems begin to integrate more and more functionality, GPU based architectures are becoming an attractive option for them. However, in a real-time system, predictability and temporal requirements are much more important than raw performance. While some real-time jobs may benefit from the performance that all cores of the GPU can provide, most jobs may require only a subset of cores to successfully meet their temporal requirements. In this paper, we present a schedule management framework for aperiodic soft-real-time jobs that may be used by a CPU GPU system designer/integrator to select, conFigure and deploy a suitable architectural platform and to perform concurrent scheduling of these jobs. An open-source implementation of our framework is made available on GitHub. Experimental results demonstrate the utility and robustness of our framework.