The 3-POCs Structure Based GPU Acceleration in Computational Spectral Imaging
In this paper, the computational spectral imaging system is reexamined, and the most time-consuming module, namely the two-step iterative shrinkage/thresholding (TwIST) reconstruction, is accelerated by GPU. The acceleration can be roughly divided into two level: 1) data parallelization and 2)operation parallelization. Data parallelization: by discovering that the observation data array in computational spectral imaging is independent with each other in the norm of the dispersion direction, we propose a strategy to decompose the large scale 2-D observation data array into several small scale overlapped stripes. Considering that the complexity for TwIST is highly non-linear, this divide-conquer strategy can significantly reduce the executing time of TwIST. 2)operation parallelization: we proposed a new 3-projection onto convex sets (POCs) structure for the GPU implementation of TwIST. All the operations are classified into 3 sets which only consist of independent matrix/vector operations. With the above two contributions, the proposed 3-POCs structure based parallel implementation achieves more than 100 times ratio than the CPU based version.