Performance evaluation in the reconstruction of 2D images of computed tomography using massively parallel programming CUDA

Abstract

Purpose: analysis of processing time and similarity of images generated between CPU and GPU architectures and sequential and parallel programming methodologies. Material and methods: for image processing a computer with AMD FX-8350 processor and an Nvidia GTX 960 Maxwell GPU was used, along with the CUDAFY library and the programming language C# with the IDE Visual studio. Results: the results of the comparisons indicate that the form of sequential programming in a CPU generates reliable images at a high custom of time when compared to the forms of parallel programming in CPU and GPU. While parallel programming generates faster results, but with increased noise in the reconstructed image. For data types float a GPU obtained best result with average time equivalent to 1/3 of the processor, however the data is of type double the parallel CPU approach obtained the best performance. Conclusion: for the float data type, the GPU had the best average time performance, while for the double data type the best average time performance was for the parallel approach CPU. Regarding image quality, the sequential approach obtained similar outputs, while theparallel approaches generated noise in their outputs.