A study on a computational model for GPGPU algorithms and its application to medical image processing

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The purpose of this research is to investigate to what extent medical image processing can be sped up on a single PC with a GPU as opposed to a PC cluster. For this, we developed two programs for cone-beam CT (computed tomography) image reconstruction-one using OpenGL/Cg and one using CUDA. For 360 images of 512 x 512 pixels each, we reconstructed a 512x512x512 voxel volume. Execution time for the OpenGL/Cg version was 8.3 seconds, and for the CUDA version was 5.7 seconds. The OpenGL/Cg version was 23.7 times faster than the CPU version (in other words, it has equivalent power of at least a 24 PC cluster). Therefore we confirmed that a single PC with GPU can provide enough computing power for real-time operation of a CT machine. Starting in April 2008, a CT machine using this algorithm was being sold by the Shimadzu Corporation.Programs that requires much CPU time are said to have a "calculation bottleneck", and the current impression is that CPU execution speed is the main bottleneck in computation. However, even among these types of programs there is a not-insignificant number which have a "memory bottleneck". Based on last years success and results from the GPGPU performance model tested last year, we are confident that GPGPU speed improvements will also be applicable to such "memory bottleneck" programs.In addition to cone-beam CT image reconstruction, we also used CUDA to implement a biology oriented array alignment program, as well as a shortest-path-between-all-points implementation for graph theory. Run time of the array alignment program was substantially decreased on GPU compared to CPU, with a 1024 length query taking about 20 seconds on the GPU compared to 677 seconds on the CPU. A query length of 1024 is suitable for work with amino acids and we feel this will contribute to pharmaceutical research.