Online Task Scheduling for LiDAR Data Preprocessing on Hybrid GPU/CPU Devices: A Reinforcement Learning Approach
Graphics processing units, Processor scheduling, Optimal scheduling, Remote sensing, Laser radar, Data preprocessing, Atomic measurements
College of Natual Science and Mathematics, Geography and the Environment
In recent years, general-purpose graphics processing units (GP-GPUs) have steadily risen in popularity for remote sensing data processing. Interest has been growing in using hybrid GPU/CPU architectures to realize the full potential of computing devices. This paper studies LiDAR data preprocessing, which is a typical data-intensive remote sensing application. It is proposed to develop an online task scheduler for hybrid GPU/CPU systems using reinforcement learning. At the core of the task scheduler is a Q-learning module that can create the optimal task execution path according to rewards accumulated over time. Constraints and preferences are also encapsulated in the scheduler to support automatic online resource scheduling. Quantitative evaluation on a typical LiDAR data set demonstrates the usefulness and potential of this online task scheduling approach for remote sensing applications.
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Tong Zhang, and Jing Li. “Online Task Scheduling for LiDAR Data Preprocessing on Hybrid GPU/CPU Devices: A Reinforcement Learning Approach.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 8, no. 1, 2015, pp. 386–397. doi: 10.1109/jstars.2015.2390626.