Compute Unified Device Architecture Platform Research Articles

Prediction of Gamma Ray data from pre-stack seismic reflection partial angle stacks using Continuous Wavelet Transform and convolutional neural network approach

Seismic inversion is one of the critical issues for geophysicists in the oil and gas industry. It is commonly used to estimate the distribution of facies-types and fluids in reservoirs across the value chain from exploration to development. However, the low vertical and spatial resolution of seismic data leads to large uncertainties in these estimations, which makes direct use of inversion results challenging. The objective of this study was the estimation of a 3D volume of Gamma Ray data from pre-stack seismic reflectivity partial angle stacks based on the Convolutional Neural Network architecture by representing data through the Continuous Wavelet Transform. The proposed methodology used 132 deviated wells from the Azeri and Chirag parts of the Azeri – Chirag – Gunashli field in the South Caspian Basin as training data. Blind tests show promising Gamma Ray predictions. To optimize the predictions, sensitivity analysis was performed on the Continuous Wavelet Transform parameters (wavelet types, range of pseudo-frequencies, etc.), the Convolutional Neural Network architecture (number of convolutional and pooling layers, dropout, activation functions) and the network parameters (batch size, number of epochs, optimizers, etc.). Due to the vast amount of data required to speed up the learning process, simulations were performed on a Graphics Processing Unit based on the Compute Unified Device Architecture platform.

Parallel Algorithms of Well-Balanced and Weighted Average Flux for Shallow Water Model Using CUDA

Compute Unified Device Architecture (CUDA) implementations are presented of a well-balanced finite volume method for solving a shallow water model. The CUDA platform allows programs to run parallel on GPU. Four versions of the CUDA algorithm are presented in addition to a CPU implementation. Each version is improved from the previous one. We present the following techniques for optimizing a CUDA program: limiting register usage, changing the global memory access pattern, and using loop unroll. The accuracy of all programs is investigated in 3 test cases: a circular dam break on a dry bed, a circular dam break on a wet bed, and a dam break flow over three humps. The last parallel version shows 3.84x speedup over the first CUDA implementation. We use our program to simulate a real-world problem based on an assumed partial breakage of the Srinakarin Dam located in Kanchanaburi province, Thailand. The simulation shows that the strong interaction between massive water flows and bottom elevations under wet and dry conditions is well captured by the well-balanced scheme, while the optimized parallel program produces a 57.32x speedup over the serial version.

Research on EBE-FEM Parallel Algorithm Combined with Fast Color Marking Method Based on CUDA Platform

The element-by-element finite element method (EBE-FEM) parallel algorithm has been realized on Compute Unified Device Architecture (CUDA) platform in this paper. An improved fast color marking method (FCM) combined with tabu search algorithm is proposed to solve the problem that the elements sharing a node wait for accessing the same memory space in parallel computation. The elements in the same color can be processed at the same time without waiting. This method can get more even color grouping faster than the classical coloring method (CCM). Combining it with the EBE parallel algorithm can achieve faster element-level operations. The validity and accuracy of the method has been verified by comparing the computed results with the analytical solution of the magnetic field produced by the solenoid. The parallel program is applied to analyze the main magnetic field of a single-phase transformer, which shows higher speedup performance.

Safer and More Efficient Parallel Cryptographic Algorithm and its Implementation in the GPU

In the digital world, the demand for data security during communication has increased. Hash functions are one of the cryptographic algorithms that provide data security in terms of data authenticity and integrity. Nowadays, most online applications require user authentication. These authentications are done on the server-side, which he must manage. As the number of applications increases, building a one-way function will be faster for calculating a hash value for small data such as passwords. In this paper, we will present a sequential cryptographic algorithm and its parallel implementation. We performed security analyses, executed comparisons for different amounts of data, and provided steps for further developing this algorithm. With the construction of this one-way function, we have provided the calculation of hash value in a shorter time for data in small quantities, which speeds up the authentication process on the server and thus speeds up the online services provided by the respective applications. A comparison was made between sequential implementation, parallel implementation on the CPU, and parallel implementation on the GPU using CUDA (Computer Unified Device Architecture) platform.

An Efficient Acceleration of Solving Heat and Mass Transfer Equations with the Second Kind Boundary Conditions in Capillary Porous Radially Composite Cylinder Using Programmable Graphics Hardware

With the recent developments in computing technology, increased efforts have gone into the simulation of various scientific methods and phenomenon in engineering fields. One such case is the simulation of heat and mass transfer equations which is becoming more and more important in analyzing various scenarios in engineering applications. Analysing the heat and mass transfer phenomenon under various environmental conditions require us to simulate it. However, this process of numerical solution of heat and mass transfer equations is very time consuming. Therefore, this paper aims at utilizing one of the acceleration techniques developed in the graphics community that exploits a graphics processing unit (GPU) which is applied to the numerical solutions of heat and mass transfer equations. The nVidia Compute Unified Device Architecture (CUDA) programming model can be a good method of applying parallel computing to program the graphical processing unit. This paper shows a good improvement in the performance, while solving the heat and mass transfer equations for a capillary porous radially composite cylinder with the second kind of boundary conditions, numerically running on GPU. This heat and mass transfer simulation is implemented using CUDA platform on nVidia Quadro FX 4800 graphics card. Our experimental results depict the drastic performance improvement when GPU is used to perform heat and mass transfer simulation. GPU can significantly accelerate the performance with a maximum observed speedup of more than 8 fold times. Therefore, the GPU is a good approach to accelerate the heat and mass transfer simulation.

An Efficient Graphics Processing Unit Scheme for Complex Geometry Simulations Using the Lattice Boltzmann Method

The lattice Boltzmann method has been fully discretized in space, time, and velocity; its inherent parallelism makes it outstanding for use in accelerated computation by graphics processing unit in large-scale simulations of fluid dynamics. When the lattice Boltzmann method is used to simulate a fluid system with complex geometry, the flow field is usually compressed to reduce memory consumption, and fluid nodes are accessed indirectly to improve computational efficiency. We designed a pointer array that is the same size as the flow field and is based on the Compute Unified Device Architecture platform’s unified memory technology. The addresses of the fluid nodes are stored in this array, and the other nodes, which are unallocated, are marked as null. For obtaining the coordinates of the fluid nodes in the original flow field, we stored the addresses of the pointer array units whose values were not null as part of the lattice attribute at the end of the lattice attribute array, forming a cyclic pointer structure to track geometric information. We validated the feasibility of this addressing scheme using an experimental simulation of aqueous humor in the anterior segment of the eye, and tested its performance on the graphics processing unit of Pascal, Volta, and Turing architecture. The present method carefully distributes data to generate fewer memory transactions and to reduce access times of the global memory, thus achieving approximately 18% performance improvement.

An Efficient Acceleration of Solving Heat and Mass Transfer Equations with the Second Kind Boundary Conditions in Capillary Porous Composite Cylinder Using Programmable Graphics Hardware

With the recent developments in computing technology, increased efforts have gone into simulation of various scientific methods and phenomenon in engineering fields. One such case is the simulation of heat and mass transfer in capillary porous media, which is becoming more and more important in analysing various scenarios in engineering applications. Analysing such heat and mass transfer phenomenon in a given environment requires us to simulate it. This entails simulation of coupled heat mass transfer equations. However, this process of numerical solution of heat and mass transfer equations is very much time consuming. Therefore, this paper aims at utilizing one of the acceleration techniques developed in the graphics community that exploits a graphics processing unit (GPU) which is applied to the numerical solutions of heat and mass transfer equations. The nVidia Compute Unified Device Architecture (CUDA) programming model caters a good method of applying parallel computing to program the graphical processing unit. This paper shows a good improvement in the performance while solving the heat and mass transfer equations for capillary porous composite cylinder with the second kind of boundary conditions numerically running on GPU. This heat and mass transfer simulation is implemented using CUDA platform on nVidia Quadro FX 4800 graphics card. Our experimental results depict the drastic performance improvement when GPU is used to perform heat and mass transfer simulation. GPU can significantly accelerate the performance with a maximum observed speedup of more than 7-fold times. Therefore, the GPU is a good approach to accelerate the heat and mass transfer simulation.

A New Parallel Approach for Accelerating the GPU-Based Execution of Edge Detection Algorithms

Real-time image processing is used in a wide variety of applications like those in medical care and industrial processes. This technique in medical care has the ability to display important patient information graphi graphically, which can supplement and help the treatment process. Medical decisions made based on real-time images are more accurate and reliable. According to the recent researches, graphic processing unit (GPU) programming is a useful method for improving the speed and quality of medical image processing and is one of the ways of real-time image processing. Edge detection is an early stage in most of the image processing methods for the extraction of features and object segments from a raw image. The Canny method, Sobel and Prewitt filters, and the Roberts' Cross technique are some examples of edge detection algorithms that are widely used in image processing and machine vision. In this work, these algorithms are implemented using the Compute Unified Device Architecture (CUDA), Open Source Computer Vision (OpenCV), and Matrix Laboratory (MATLAB) platforms. An existing parallel method for Canny approach has been modified further to run in a fully parallel manner. This has been achieved by replacing the breadth- first search procedure with a parallel method. These algorithms have been compared by testing them on a database of optical coherence tomography images. The comparison of results shows that the proposed implementation of the Canny method on GPU using the CUDA platform improves the speed of execution by 2-100× compared to the central processing unit-based implementation using the OpenCV and MATLAB platforms.

Effective Connectivity Analysis in Brain Networks: A GPU-Accelerated Implementation of the Cox Method

The observation of interactions between neurons of a network can reveal important information about how information is processed within that network. Such observation can be established with the analysis of causality between the activities of the different neurons in the network. This analysis is called effective connectivity analysis. However, methods for such analysis are either computationally heavy for daily use or too inaccurate for making reliable analyses. Cox method produces reliable analysis, but the computation takes hours on CPUs, making it slow to use on research. In this paper, two algorithms are presented that speed up analysis of Cox method by parallelizing the computation on a graphical processing unit (GPU) with the help of a Compute Unified Device Architecture platform. Both algorithms are evaluated according to the network size and recording duration. The interest of proposing GPU implementations is in gaining the computation time but another important interest is that such implementation requires rethinking the algorithm in different ways than as the sequential implementation. This rethinking itself brings new optimization possibilities, e.g. by employing OpenCL. Utilizing this accelerated implementation, the Cox method is then applied on an experimental dataset from CRCNS in a personal computer. This should facilitate observations of biological neural network organizations that can provide new insights to improve understanding of memory, learning and intelligence.

Numerical Solutions of Heat and Mass Transfer with the First Kind Boundary and Initial Conditions in Capillary Porous Cylinder Using Programmable Graphics Hardware

Recently, heat and mass transfer simulation is more and more important in various engineering fields. In order to analyze how heat and mass transfer in a thermal environment, heat and mass transfer simulation is needed. However, it is too much time-consuming to obtain numerical solutions to heat and mass transfer equations. Therefore, in this paper, one of acceleration techniques developed in the graphics community that exploits a graphics processing unit (GPU) is applied to the numerical solutions of heat and mass transfer equations. The nVidia Compute Unified Device Architecture (CUDA) programming model provides a straightforward means of describing inherently parallel computations. This paper improves the performance of solving heat and mass transfer equations over capillary porous cylinder with the first boundary and initial conditions numerically running on GPU. Heat and mass transfer simulation using the novel CUDA platform on nVidia Quadro FX 4800 is implemented. Our experimental results clearly show that GPU can accurately perform heat and mass transfer simulation. GPU can significantly accelerate the performance with the maximum observed speedups 10 times. Therefore, the GPU is a good approach to accelerate the heat and mass transfer simulation

Graphical processing unit‐based parallelization of the Open Shortest Path First and Border Gateway Protocol routing protocols

SUMMARYExponentially growing number of devices on Internet incurs an ever‐increasing load on the network routers in executing network protocols. Parallel processing has recently become an unavoidable means to scale up the router performance. The research effort elaborated in this paper is focused on exploiting the modern trends of general‐purpose computing on graphics processing unit computing in speeding up the execution of network protocols. An additional benefit is off‐loading the CPU, which can now be fully dedicated to the packet processing and forwarding. To this end, the Shortest Path First algorithm in the Open Shortest Path First protocol and the choice of the best routes in the Border Gateway Protocol are parallelized for efficient execution on Compute Unified Device Architecture platform. An evaluation study was conducted on three different graphics processing units with representative network workload for a varying number of routes and devices. The obtained speedup results confirmed the viability and cost‐effectiveness of such an approach. Copyright © 2014 John Wiley & Sons, Ltd.

Accelerated catadioptric omnidirectional view image unwrapping processing using GPU parallelisation

Catadioptric omnidirectional view sensors have found increasing adoption in various robotic and surveillance applications due to their 360° field of view. However, the inherent distortion caused by the sensors prevents their direct utilisations using existing image processing techniques developed for perspective images. Therefore, a correction processing known as "unwrapping" is commonly performed. However, the unwrapping process incurs additional computational loads on central processing units. In this paper, a method to reduce this burden in the computation is investigated by exploiting the parallelism of graphical processing units (GPUs) based on the Compute Unified Device Architecture (CUDA). More specifically, we first introduce a general approach of parallelisation to the said process. Then, a series of adaptations to the CUDA platform is proposed to enable an optimised usage of the hardware platform. Finally, the performances of the unwrapping function were evaluated on a high-end and low-end GPU to demonstrate the effectiveness of the parallelisation approach.

A Novel Method of Parallel Computation for the Whole Scene Test in Power System

In this paper, design and implementation of a new parallel computing method based on CUDA (Compute Unified Device Architecture) platform is described in detail. The method includes algorithm of matrix fraction and partial LU decomposition that are used to support parallel computing for simulation of the whole scene test in power system. The paper describes all the steps of algorithm implementation for deploying the software with CUDA technology. And performances are evaluated by test, in which two Jacobi matrix equations of power flow calculation with different dimension are selected as the test case. The results are compared with that of serial computing and MPI (Message Passing Interface). The results show that the method proposed in this paper has better performance of acceleration on CUDA platform, which can support the simulation of the whole scene test on wide-area power grid.

연속 영상 기반 실시간 객체 분할

This paper shows an approach for real-time object segmentation on GPU (Graphics Processing Unit) using CUDA (Compute Unified Device Architecture). Recently, many applications that is monitoring system, motion analysis, object tracking or etc require real-time processing. It is not suitable for object segmentation to procedure real-time in CPU. NVIDIA provide CUDA platform for Parallel Processing for General Computation to upgrade limit of Hardware Graphic. In this paper, we use adaptive Gaussian Mixture Background Modeling in the step of object extraction and CCL(Connected Component Labeling) for classification. The speed of GPU and CPU is compared and evaluated with implementation in Core2 Quad processor with 2.4GHz.The GPU version achieved a speedup of 3x-4x over the CPU version.

Parallel data mining techniques on Graphics Processing Unit with Compute Unified Device Architecture (CUDA)

Recent development in Graphics Processing Units (GPUs) has enabled inexpensive high performance computing for general-purpose applications. Compute Unified Device Architecture (CUDA) programming model provides the programmers adequate C language like APIs to better exploit the parallel power of the GPU. Data mining is widely used and has significant applications in various domains. However, current data mining toolkits cannot meet the requirement of applications with large-scale databases in terms of speed. In this paper, we propose three techniques to speedup fundamental problems in data mining algorithms on the CUDA platform: scalable thread scheduling scheme for irregular pattern, parallel distributed top-k scheme, and parallel high dimension reduction scheme. They play a key role in our CUDA-based implementation of three representative data mining algorithms, CU-Apriori, CU-KNN, and CU-K-means. These parallel implementations outperform the other state-of-the-art implementations significantly on a HP xw8600 workstation with a Tesla C1060 GPU and a Core-quad Intel Xeon CPU. Our results have shown that GPU + CUDA parallel architecture is feasible and promising for data mining applications.

Tabu Search with two approaches to parallel flowshop evaluation on CUDA platform

The introduction of NVidia’s powerful Tesla GPU hardware and Compute Unified Device Architecture (CUDA) platform enable many-core parallel programming. As a result, existing algorithms implemented on a GPU can run many times faster than on modern CPUs. Relatively little research has been done so far on GPU implementations of discrete optimisation algorithms. In this paper, two approaches to parallel GPU evaluation of the Permutation Flowshop Scheduling Problem, with makespan and total flowtime criteria, are proposed. These methods can be employed in most population-based algorithms, e.g. genetic algorithms, Ant Colony Optimisation, Particle Swarm Optimisation, and Tabu Search. Extensive computational experiments, on Tabu Search for Flowshop with both criteria, followed by statistical analysis, confirm great computational capabilities of GPU hardware. A GPU implementation of Tabu Search runs up to 89 times faster than its CPU counterpart.

Coherence parallel algorithm of seismic data processing based on CUDA

In seismic exploration interpretation,the application of coherent technology can clearly identify faults and stratigraphy,but the traditional calculation method cannot meet the need of the coherence body calculation from 3D seismic data.Based on CUDA(Compute Unified Device Architecture) platform,a coherence parallel algorithm was proposed.It could accelerate the speed of matrix multiplication with the performance of GPU cluster.Extensive experiments have been conducted in a PC with Intel Core2Due CPU and NVIDIA GeForce 8800 GT graphic card,and the results prove the efficiency of the proposed algorithm.Even though the actual speed-ups in production codes will vary with the particular problem,the results obtained here indicate that GPU can potentially be a very useful platform for processing large-scale seismic data.