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Real-Time GPU Scheduling with Preemption Support for Autonomous Mobile Robots

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dc.contributor.authorBharmal, Burhanuddin Asifhusainen
dc.contributor.committeechairZeng, Haiboen
dc.contributor.committeememberWilliams, Ryan K.en
dc.contributor.committeememberMin, Chang Wooen
dc.contributor.departmentElectrical and Computer Engineeringen
dc.date.accessioned2022-01-18T16:32:11Zen
dc.date.available2022-01-18T16:32:11Zen
dc.date.issued2022-01-18en
dc.description.abstractThe use of graphical processing units (GPUs) for autonomous robots has grown recently due to their efficiency and suitability for data intensive computation. However, the current embedded GPU platforms may lack sufficient real-time capabilities for safety-critical autonomous systems. The GPU driver provides little to no control over the execution of the computational kernels and does not allow multiple kernels to execute concurrently for integrated GPUs. With the development of modern embedded platforms with integrated GPU, many embedded applications are accelerated using GPU. These applications are very computationally intensive, and they often have different criticality levels. In this thesis, we provide a software-based approach to schedule the real-world robotics application with two different scheduling policies: Fixed Priority FIFO Scheduling and Earliest Deadline First Scheduling. We implement several commonly used applications in autonomous mobile robots, such as Path Planning, Object Detection, and Depth Estimation, and improve the response time of these applications. We test our framework on NVIDIA AGX Xavier, which provides high computing power and supports eight different power modes. We measure the response times of all three applications with and without the scheduler on the NVIDIA AGX Xavier platform on different power modes, to evaluate the effectiveness of the scheduler.en
dc.description.abstractgeneralAutonomous mobile robots for general human services have increased significantly due to ever-growing technology. The common applications of these robots include delivery services, search and rescue, hotel services, and so on. This thesis focuses on implementing the computational tasks performed by these robots as well as designing the task scheduler, to improve the overall performance of these tasks. The embedded hardware is resource-constrained with limited memory, power, and operating frequency. The use of a graphical processing unit (GPU) for executing the tasks to speed up the operation has increased with the development of the GPU programming framework. We propose a software-based GPU scheduler to execute the functions on GPU and get the best possible performance from the embedded hardware.en
dc.description.degreeMaster of Scienceen
dc.format.mediumETDen
dc.identifier.othervt_gsexam:33830en
dc.identifier.urihttp://hdl.handle.net/10919/107756en
dc.language.isoenen
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectRT-GPU Schedulingen
dc.subjectLimited Preemptionen
dc.subjectPath Planningen
dc.subjectObject Detectionen
dc.subjectDepth Estimationen
dc.titleReal-Time GPU Scheduling with Preemption Support for Autonomous Mobile Robotsen
dc.typeThesisen
thesis.degree.disciplineComputer Engineeringen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen

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