scholarly journals A REVIEW ON IMAGE SEGMENTATION USING GPU

2016 ◽  
Vol 15 (10) ◽  
pp. 7160-7163
Author(s):  
Gurpreet Kaur ◽  
Sonika Jindal
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Image Segmentation ◽  
Graphics Processing Unit ◽  
Processing Unit ◽  
System Resources ◽  
Cuda Architecture ◽  
Graphics Processing

Image Segmentations play a heavy role in areas such as computer vision and image processing due to its broad usage and immense applications. Because of the large importance of image segmentation a number of algorithms have been proposed and different approaches have been adopted. Segmentation divides an image into distinct regions containing each pixel with similar attributes. The objective of apportioning is to simplify and/or alter the representation of an image into something that is more meaningful and more comfortable to break down. This paper discusses the various techniques implemented for image segmentation and discusses the various Computations that can be performed on the graphics processing unit (GPU) by means of the CUDA architecture in order to achieve fast performance and increase the utilization of available system resources.

scholarly journals Analysis of Fast Fourier Transformations algorithm for CUDA Architecture

2012 ◽  
Vol 53 ◽  
Author(s):  
Beatričė Andziulienė ◽  
Evaldas Žulkas ◽  
Audrius Kuprinavičius
Keyword(s):  
Graphics Processing Unit ◽  
General Purpose ◽  
Fast Fourier Transformation ◽  
Processing Unit ◽  
Data Allocation ◽  
Analysis Method ◽  
Central Processing ◽  
Execution Speed ◽  
Cuda Architecture ◽  
Graphics Processing

In this work Fast Fourier transformation algorithm for general purpose graphics processing unit processing (GPGPU) is discussed. Algorithm structure and individual stages performance were analysed. With performance analysis method algorithm distribution and data allocation possibilities were determined, depending on algorithm stages execution speed and algorithm structure. Ratio between CPU and GPU execution during Fast Fourier transform signal processing was determined using computer-generated data with frequency. When adopting CPU code for CUDA execution, it not becomes more complex, even if stream procesor parallelization and data transfering algorith stages are considered. But central processing unit serial execution).

Advanced Topics GPU Programming and CUDA Architecture

2016 ◽  
pp. 175-203
Author(s):  
Mainak Adhikari ◽  
Sukhendu Kar
Keyword(s):  
Graphics Processing Units ◽  
High Performance ◽  
Programming Model ◽  
Graphics Processing Unit ◽  
Direct Access ◽  
Gpu Programming ◽  
Processing Unit ◽  
Computing Platform ◽  
Cuda Architecture ◽  
Graphics Processing

Graphics processing unit (GPU), which typically handles computation only for computer graphics. Any GPU providing a functionally complete set of operations performed on arbitrary bits can compute any computable value. Additionally, the use of multiple graphics cards in one computer, or large numbers of graphics chips, further parallelizes the already parallel nature of graphics processing. CUDA (Compute Unified Device Architecture) is a parallel computing platform and programming model created by NVIDIA and implemented by the graphics processing units (GPUs). CUDA gives program developers direct access to the virtual instruction set and memory of the parallel computational elements in CUDA GPUs. This chapter first discuss some features and challenges of GPU programming and the effort to address some of the challenges with building and running GPU programming in high performance computing (HPC) environment. Finally this chapter point out the importance and standards of CUDA architecture.

scholarly journals Parallel SVD Algorithm for a Three-Diagonal Matrix on a Video Card Using the Nvidia CUDA Architecture

2021 ◽  
Vol 4 ◽  
pp. 16-22
Author(s):  
Mykola Semylitko ◽  
Gennadii Malaschonok
Keyword(s):  
Graphics Processing Units ◽  
Graphics Processing Unit ◽  
Computation Time ◽  
Application Programming Interface ◽  
General Purpose ◽  
Diagonal Matrix ◽  
Free Access ◽  
Processing Unit ◽  
Cuda Architecture ◽  
Graphics Processing

SVD (Singular Value Decomposition) algorithm is used in recommendation systems, machine learning, image processing, and in various algorithms for working with matrices which can be very large and Big Data, so, given the peculiarities of this algorithm, it can be performed on a large number of computing threads that have only video cards.CUDA is a parallel computing platform and application programming interface model created by Nvidia. It allows software developers and software engineers to use a CUDA-enabled graphics processing unit for general purpose processing – an approach termed GPGPU (general-purpose computing on graphics processing units). The GPU provides much higher instruction throughput and memory bandwidth than the CPU within a similar price and power envelope. Many applications leverage these higher capabilities to run faster on the GPU than on the CPU. Other computing devices, like FPGAs, are also very energy efficient, but they offer much less programming flexibility than GPUs.The developed modification uses the CUDA architecture, which is intended for a large number of simultaneous calculations, which allows to quickly process matrices of very large sizes. The algorithm of parallel SVD for a three-diagonal matrix based on the Givents rotation provides a high accuracy of calculations. Also the algorithm has a number of optimizations to work with memory and multiplication algorithms that can significantly reduce the computation time discarding empty iterations.This article proposes an approach that will reduce the computation time and, consequently, resources and costs. The developed algorithm can be used with the help of a simple and convenient API in C ++ and Java, as well as will be improved by using dynamic parallelism or parallelization of multiplication operations. Also the obtained results can be used by other developers for comparison, as all conditions of the research are described in detail, and the code is in free access.

scholarly journals A NewSpeed Power Area and Accuracy (SPAA) Aware Cordic Processing Unit By Vedic Mathematics For The Application of Computer Vision

2021 ◽  
Vol 12 (2) ◽  
pp. 3199-3209
Author(s):  
Nitesh Kumar Sharma, Et. al.
Keyword(s):  
Computer Vision ◽  
High Speed ◽  
Graphics Processing Unit ◽  
Processing Unit ◽  
Arithmetic Unit ◽  
Vedic Mathematics ◽  
Fast Processing ◽  
Calculation Processing ◽  
Graphics Processing ◽  
The Brain

we are living in the era of fast processing applications like 3D, 5G, 9D. These types of application need a processing unit which have separate arithmetic unit & separate trigonometric unit which is well known as CORDIC processing unit. As we know Graphics processing unit is the brain of any graphics systems now a days there is Gaming specific systems are available which require ultra-high-speed GPU on those GPU there is separate trigonometric calculation processing unit is there which is called CORDIC. So, in this paper basically we proposed a novel architecture of CORDIC unit which is able to give the output in very less time. In this paper we also try to do the justice with the speed power area and accuracy Metrix.

History and Evolution of GPU Architecture

2016 ◽  
pp. 109-135
Author(s):  
Prashanta Kumar Das ◽  
Ganesh Chandra Deka
Keyword(s):  
Image Processing ◽  
Graphics Processing Unit ◽  
Hardware Architecture ◽  
Central Processing Unit ◽  
Processing Unit ◽  
3D Image ◽  
Central Processing ◽  
Graphics Processing ◽  
Graphics Engine ◽  
Gpu Architecture

The Graphics Processing Unit (GPU) is a specialized and highly parallel microprocessor designed to offload 2D/3D image from the Central Processing Unit (CPU) to expedite image processing. The modern GPU is not only a powerful graphics engine, but also a parallel programmable processor with high precision and powerful features. It is forcasted that by 2020, 48 Core GPU will be available while by 2030 GPU with 3000 core is likely to be available.This chapter describes the chronology of evolution of GPU hardware architecture and the future ahead.

scholarly journals Underwater Image Processing using Graphics Processing Unit (GPU)

2020 ◽  
Vol 32 ◽  
pp. 03041
Author(s):  
Sayooj Ottapura ◽  
Rahul Mistry ◽  
Jatin Keni ◽  
Chaitanya Jage
Keyword(s):  
Image Processing ◽  
Signal Processing ◽  
Graphics Processing Unit ◽  
Computation Time ◽  
Processing Unit ◽  
Blue Colour ◽  
Underwater Image ◽  
Colour Distribution ◽  
Graphics Processing ◽  
Underwater Image Processing

Image processing is a method used for enhancement of an image or to extract some useful information from the image. It is a type of signal processing in which input is an image and output may be an image or any characteristics/features associated with that image. In this paper we will be focusing on a specific type of Image Processing i.e. Underwater Image Processing. Underwater Image Processing has always faced the problem of imbalance in colour distribution and this problem can be tackled by the simplest algorithm for colour balancing. We will be proceeding with the assumption that the highest values of R, G, B observed in the image corresponds to white and the lowest values corresponds to darkness. The underwater images are majorly saturated by blue colour because of its short wavelength and in this paper, we aim to enhance the image. We proposed a colour balancing algorithm for normalizing the image. The entire process will first be carried out on a CPU followed by a GPU. We will then compare the speedup obtained. Speedup is an important parameter in the field on image processing since a better speedup can help reduce the computation time significantly while maintaining a higher efficiency.

scholarly journals Image Classification using Parallel CPU and GPU Computing

2020 ◽  
Vol 9 (4) ◽  
pp. 839-843
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Gpu Computing ◽  
Graphics Processing Unit ◽  
Computational Time ◽  
Processing Unit ◽  
System Memory ◽  
Graphics Processing

Image classification algorithms such as Convolutional Neural Network used for classifying huge image datasets takes a lot of time to perform convolution operations, thus increasing the computational demand of image processing. Compared to CPU, Graphics Processing Unit (GPU) is a good way to accelerate the processing of the images. Parallelizing multiple CPU cores is also another way to process the images faster. Increasing the system memory (RAM) can also decrease the computational time of image processing. Comparing the architecture of CPU and GPU, the former consists of a few cores optimized for sequential processing whereas the later has thousands of relatively simple cores clocked at approx. 1Ghz. The aim of this project is to compare the performance of parallelized CPUs and a GPU. Python’s Ray library is being used to parallelize multicore CPUs. The benchmark image classification algorithm used in this project is Convolutional Neural Network. The dataset used in this project is Plant Disease Image Dataset. Our results show that the GPU implementation achieves 80% speedup compared to the CPU implementation.

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