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

TL;DR

This study compares CPU and GPU-based parallel and sequential programming methods for 2D computed tomography image reconstruction, analyzing processing time and image quality differences using CUDA and C#.

Contribution

It provides a comparative analysis of CPU and GPU parallel processing effects on CT image reconstruction quality and performance, highlighting trade-offs between speed and image noise.

Findings

Sequential CPU processing yields reliable images with longer processing times.

GPU parallel processing is faster but introduces more noise in images.

For float data, GPU outperforms CPU in speed; for double data, CPU is faster.

Abstract

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.