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Dynamic schedule management framework for aperiodic soft-real-time jobs on GPU based architectures

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.