Cloud-based fine-grained parallel optimization on cpu-gpu heterogeneous hyperspectral image superpixel space-spectrum fusion classification algorithms

To meet the need for efficient execution of hyperspectral remote sensing image classification algorithms, this paper proposes a fine-grained parallel optimization method for a CPU-GPU heterogeneous hyperspectral image superpixel spectral fusion classification algorithm based on cloud computing. Ray is used as the distributed computing engine to fully utilize the logical control ability and large-scale parallel computing ability of the CPU-GPU heterogeneous platform. We first decouple the superpixel spectral fusion classification algorithm, analyze the data dependence and computing characteristics of sub-tasks, use the GPU to accelerate the algorithm, and then further extend the algorithm to the CPU-GPU heterogeneous platform. At the same time, we establish a scheduling model for algorithm task scheduling problems, specifying the value of the parallelism degree for the algorithm in a fine-grained manner. It is verified by experiments that the parallelization method proposed in this paper can effectively improve the execution efficiency with the premise of the accuracy unreduced.