Feasibility of parallel algorithms and graphics processing unit acceleration in chemical imaging

Chemical imaging is an important area of Near Infrared (NIR) spectroscopy gathering a large amount of data. One of its challenges is the need for real time analysis and display. Data loss will occur when the average data acquisition rate is higher than the processing rate. This is very presumable when the spectral calculations are done in the traditional way (sequentially) on every pixel. The needed time can be shortened either by using faster processors or by distributing the computation into parallel executable parts. As the processor speed is generally limited by the available technology, the parallel computation is the only feasible improvement. Among the possible alternatives the graphics processing unit (GPU) offers a flexible, cost-effective and efficient solution.

This study aims at effective parallelizing of the most important algorithms in chemical imaging using a general purpose GPU. This will be demonstrated by a comparison of execution times of sequential and parallel calculations.