REAL-TIME HYPERSPECTRAL IMAGE ANALYSIS ON GPU HARDWARE : Performance impact of different GPU architecture implementations

Sammanfattning: This paper examines the optimization possibilities of using different GPU memory for a hyperspectral imaging algorithm. It focuses on the global memory, the shared memory, and the constant memory of the GPU. Three versions of the hyperspectral imaging algorithm are implemented utilizing the GPU’s global memory, shared memory, and constant memory, respectively. The algorithm consists of 4 steps: 3 pre-processing steps and 1 prediction step. The pre-processing step comprise of calculating the absorption, centering the image, normalizing using the standard normal variate. Lastly, there is a prediction step using a matrix-vector multiplication. Then the implementations are tested on their performance in processing an image. We also investigate how coalescing images in the different implementations can speed up the processing and what kind of extra latency it adds to the processing of an image.