  Analysis of processing time and similarity of images generated between CPU
and GPU architectures and sequential and parallel programming. 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. 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. 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 the parallel approaches generated noise in their outputs.
