Survey of using gpu cuda programming model
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With the technology development of medical industry, processing data is expanding rapidly and computation time also increases due to many factors like 3D, 4D treatment planning, the increasing sophistication of MRI pulse sequences and the growing complexity of algorithms. Graphics processing unit (GPU) addresses these problems and gives the solutions for using their features such as, high computation throughput, high memory bandwidth, support for floating-point arithmetic and low cost. Compute unified device architecture (CUDA) is a popular GPU programming model introduced by NVIDIA for parallel computing. This review paper briefly discusses the need of GPU CUDA computing in the medical image analysis. The GPU performances of existing algorithms are analyzed and the computational gain is discussed. A few open issues, hardware configurations and optimization principles of existing methods are discussed. This survey concludes the few optimization techniques with the medical imaging algorithms on GPU. Finally, limitation and future scope of GPU programming are discussed.
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Survey of using gpu cuda programming model
- 1. Survey of using GPU CUDA programming model in medical image analysis Armin shoughi May 2019 1
- 2. Introduction Computed tomography (CT) , magnetic resonance imaging (MRI) , positron emission tomography (PET) and ultrasound are famous medical modalities that produce the 2D, 3D and 4D types of medical images which are guiding the diagnosis process and treatment planning. GPU is highly parallel, multithread, multiple core processors and has high memory bandwidth to give the solution to the computational problems. 2
- 3. Overview of GPU computing model – CUDA 3 • NVIDIA has introduced its own massively parallel architecture called compute unified device architecture (CUDA) in 2006 and made the evolution in GPU programming model. • CUDA is an open source and extension of the C programming language. • CUDA program contains two phases that are executed in either host (CPU) or device (GPU). • There is no data parallelism in the host code.
- 4. GPU - CUDA architecture 1. programming model 2. memory model 3. CUDA work flow
- 5. GPU - CUDA programming model 5 GPU - CUDA hardware builds with three main parts to utilize effectively the full computation capability of GPU. The grids, blocks and threads build the CUDA architecture.
- 6. GPU- CUDA memory model 6 A GPU has M number of streaming multiprocessors (SM) and N number of streaming processor cores (SPs) to each SM. Each thread can access variables from the local memory and registers. Registers have the largest bandwidth and frequently accessed variables are stored in the registers.
- 7. CUDA work flow model 7 The CUDA program starts with host execution. The kernel function generates the large amount of threads to execute data parallelism. Before starting the kernel, all the necessary data is transferred from host to allocated device memory.
- 8. GPU computation for medical image analysis • denoising • Registration • Segmentation • visualization
- 9. Image denoising 9 Image denoising is an important task in medical imaging applications in order to enhance and recover hidden details from the data. Solution of this problem may lead to improve the diagnosis and surgical procedures. The most commonly used denoising algorithms in the medical domain are : • adaptive filtering • anisotropic diffusion • bilateral filtering • non-local means filter
- 10. Adaptive filtering 10 The denoising approach uses an adaptive filter introduced by Knutsson et al., in 1983 The adaptive filter is a self-modifying digital filter that adjusts its filter coefficients in an attempt to minimize an error function. Adaptive filter is a directed method that does not need to be iterated.
- 11. Anisotropic diffusion 11 Anisotropic diffusion filter is an iterative algorithm introduced by Perona and Malik in 1987. The algorithm aims at reducing image noise without affecting significant parts of the image content, edges, regions, lines or other details .
- 12. Registration 12 The term medical image registration determines the spatial alignment between reference image and spatial transformed image. The reference image and transformed image have acquired from same or different modalities. Two popular registration algorithms are : • block matching algorithm (BMA) • rigid transformation estimation (RTE).
- 13. Block matching algorithm 13 The block-matching algorithm (BMA) is the most popular method for the motion estimation from the image sequence. This method splits an image into blocks and estimates the displacement to each block.
- 14. Rigid transformation estimation 14 Rigid Transformation Estimation (RTE) is one of the simplest forms of image registration in the medical imaging. RTE allows finding the transformation. between references and moving images with the support of vectors given by the BMA. A rigid transformation (T) represents the linear and/or angular displacement of a rigid body.
- 15. Segmentation 15 Many segmentation methods are computationally expensive while running on large amount of dataset produced by the medical modalities. Image segmentation in medical imaging is often used to segment brain structures, blood vessels, tumors and bones. The famous segmentation methods : • thresholding • region growing
- 16. Thresholding 16 Thresholding is a process to segment each pixel or voxel using one or more threshold values. Thresholding is a simplest technique to implement the data parallelism using voxel per thread in 3D image or pixel per thread in 2D image.
- 17. Region growing 17 Region growing is commonly used medical image segmentation technique. Region growing starts with initial seed point from object which is given by either manually or automatically using prior knowledge.
- 18. Visualization 18 Medical image processing combined with visualization makes new way to diagnoses and to evaluate the effect of treatment given to the patient more accurate and reliable by using computers. Image visualization categorized into two groups: • surface rendering • volume rendering