Graphics Processing Unit Parallel Computing Research Articles

Incorporation of adaptive compression into a GPU parallel computing framework for analyzing large-scale vessel trajectories

Automatic Identification System (AIS) offers a wealth of vessel navigation data, which underpins research in maritime data mining, situational awareness, and knowledge discovery within the realm of intelligent transportation systems. The flourishing marine industry has prompted AIS satellites and base stations to generate massive amounts of vessel trajectory data, escalating both data storage and calculation costs. The conventional Douglas-Peucker (DP) algorithm used for trajectory compression sets a uniform threshold, which hampers effective compression. Additionally, compressing and accelerating the computation of large datasets poses a significant challenge in real-world applications. To address these limitations, this paper aims to develop a new Graphics Processing Unit (GPU) parallel computing and compression framework that enables the acceleration of the optimal threshold calculation for each trajectory automatically in maritime big data mining. It achieves this by incorporating a new Adaptive DP with Speed and Course (ADPSC) algorithm, which utilizes the dynamic navigation characteristics of different vessels. It can effectively solve the associated computational time cost concern when using the ADPSC algorithm to compress vast trajectory datasets in the real world. Additionally, this paper proposes a novel evaluation metric for assessing compression efficacy based on the Dynamic Time Warping (DTW) method. Comprehensive experiments encompass vessel trajectory datasets from three representative research areas: Tianjin Port, Chengshan Jiao Promontory, and Caofeidian Port. The experimental results demonstrate that 1) the newly developed ADPSC method outperforms in terms of compression, and 2) the designed GPU parallel computing framework can significantly shorten the compression time for extensive datasets. The GPU-accelerated compression methodology not only minimizes storage and transmission costs for data from both manned and unmanned vessels but also enhances data processing speed, supporting real-time decision-making. From a theoretical perspective, it provides the key to the puzzle of realizing the real-time anti-collision of manned and unmanned ships, particularly in complex waters. It hence makes significant contributions to maritime safety in the autonomous shipping era.

High-efficiency computation for electromagnetic forming process: An explicit-implicit GPU approach

Low computational efficiency is one of the crucial challenges in electromagnetic forming process simulation. However, the huge gap in switching from the electromagnetic field to mechanics hinders the development of high-efficiency parallel coupling. A graphics processing unit (GPU)-based explicit-implicit coupling method is proposed to address the high-computational cost in simulations of the electromagnetic forming process, in which the large deformation (explicit) and electromagnetic field (implicit) are intimately coupled. The electromagnetic-mechanical coupling is achieved by constructing five parallel algorithms, which include the parallel operations of vector-vector, matrix-matrix, matrix-vector, vector assembly, and matrix-free assembly parallel preconditioned conjugate gradient algorithm. The high-efficiency GPU parallel computing is realized through the special data storage structure and parallel strategies. The explicit-implicit GPU method is validated against the experimental results, and several examples are presented for simulations of the electromagnetic forming process to illustrate both the accuracy and high-efficiency parallel acceleration performance of the present program.

A Hybrid GPU and CPU Parallel Computing Method to Accelerate Millimeter-Wave Imaging

The range migration algorithm (RMA) based on Fourier transformation is widely applied in millimeter-wave (MMW) close-range imaging because of its few operations and small approximation. However, its interpolation stage is not effective due to the involved intensive logic controls, which limits the speed performance in a graphics processing unit (GPU) platform. Therefore, in this paper, we present an acceleration optimization method based on the hybrid GPU and central processing unit (CPU) parallel computation for implementing the RMA. The proposed method exploits the strong logic-control capability of the CPU to assist the GPU in processing the logic controls of the interpolation stage. The common positions of wavenumber-domain components to be interpolated are calculated by the CPU and stored in the constant memory for broadcast at any time. This avoids the repetitive computation consumed in a GPU-only scheme. Then the GPU is responsible for the remaining matrix-related steps and outputs the needed wavenumber-domain values. The imaging experiments verify the acceleration efficiency of the proposed method and demonstrate that the speedup ratio of our proposed method is more than 15 times of that by the CPU-only method, and more than 2 times of that by the GPU-only method.

A Novel Method for Fast Kernel Minimum Noise Fraction Transformation in Hyperspectral Image Dimensionality Reduction

Feature extraction, aiming to simplify and optimize data features, is a typical hyperspectral image dimensionality reduction technique. As a kernel-based method, kernel minimum noise fraction (KMNF) transformation is excellent at handling the nonlinear features within HSIs. It adopts the kernel function to ensure data linear separability by transforming the original data to a higher feature space, following which a linear analysis can be performed in this space. However, KMNF transformation has the problem of high computational complexity and low execution efficiency. It is not suitable for the processing of large-scale datasets. In terms of this problem, this paper proposes a graphics processing unit (GPU) and Nyström method-based algorithm for Fast KMNF transformation (GNKMNF). First, the Nyström method estimates the eigenvector of the entire kernel matrix in KMNF transformation by the decomposition and extrapolation of the sub-kernel matrix to reduce the computational complexity. Then, the sample size in the Nyström method is determined utilizing a proportional gradient selection strategy. Finally, GPU parallel computing is employed to further improve the execution efficiency. Experimental results show that compared with KMNF transformation, improvements of up to 1.94% and 2.04% are achieved by GNKMNF in overall classification accuracy and Kappa, respectively. Moreover, with a data size of 64 × 64 × 250, the execution efficiency of GNKMNF speeds up by about 80×. The outcome demonstrates the significant performance of GNKMNF in feature extraction and execution efficiency.

Slicing Algorithm and Partition Scanning Strategy for 3D Printing Based on GPU Parallel Computing.

Aiming at the problems of over stacking, warping deformation and rapid adjustment of layer thickness in electron beam additive manufacturing, the 3D printing slicing algorithm and partition scanning strategy for numerical control systems are studied. The GPU (graphics processing unit) is used to slice the 3D model, and the STL (stereolithography) file is calculated in parallel according to the normal vector and the vertex coordinates. The voxel information of the specified layer is dynamically obtained by adjusting the projection matrix to the slice height. The MS (marching squares) algorithm is used to extract the coordinate sequence of the binary image, and the ordered contour coordinates are output. In order to avoid shaking of the electron gun when the numerical control system is forming the microsegment straight line, and reduce metal overcrowding in the continuous curve C0, the NURBS (non-uniform rational b-splines) basis function is used to perform curve interpolation on the contour data. Aiming at the deformation problem of large block components in the forming process, a hexagonal partition and parallel line variable angle scanning technology is adopted, and an effective temperature and deformation control strategy is formed according to the European-distance planning scan order of each partition. The results show that the NURBS segmentation fits closer to the original polysurface cut line, and the error is reduced by 34.2% compared with the STL file slice data. As the number of triangular patches increases, the algorithm exhibits higher efficiency, STL files with 1,483,132 facets can be cut into 4488 layers in 89 s. The slicing algorithm involved in this research can be used as a general data processing algorithm for additive manufacturing technology to reduce the waiting time of the contour extraction process. Combined with the partition strategy, it can provide new ideas for the dynamic adjustment of layer thickness and deformation control in the forming process of large parts.

GPU-Based Parallel Implementation of VLBI Correlator for Deep Space Exploration System

Very Long Baseline Interferometry (VLBI) solution can yield accurate information of angular position, and has been successfully used in the field of deep space exploration, such as astrophysics, imaging, detector positioning, and so on. The increase in VLBI data volume puts higher demands on efficient processing. Essentially, the main step of VLBI is the correlation processing, through which the angular position can be calculated. Since the VLBI correlation processing is both computation-intensive and data-intensive, the CPU cluster is usually employed in practical application to perform complex distributed computation. In this paper, we propose a parallel implementation of VLBI correlator based on graphics processing unit (GPU) to realize a more efficient and economical angular position calculation of deep space target. On the basis of massively GPU parallel computing, the coalesced access strategy and the parallel pipeline strategy are introduced to further accelerate the VLBI correlator. Experimental results show that the optimized GPU-based VLBI method can meet the real-time processing requirements of the received data stream. Compared with the sequential method, the proposed approach can reach a 224.1 × calculation speedup, and a 36.8 × application speedup. Compared with the multi-CPUs method, it can achieve 28.6 × calculation speedup and 4.7 × application speedup.

Magnetic resonance imaging simulation with spin-lock preparations to detect tiny oscillatory magnetic fields

Spin-lock preparation was studied to detect tiny oscillatory magnetic fields such as a neural magnetic field without the blood oxygen level-dependent effect. This approach is a direct measurement and independent of static magnetic field strength. Accordingly, it is anticipated as a feasible functional magnetic resonance imaging (fMRI) in low and ultra-low-field MRI. Several reports have been published on spin-lock preparation but reports on imaging simulation are rare. Research in this area can assist in investigating magnetic resonance signal changes and, accordingly, can help to develop new spin-lock methods. In this study, we propose an imaging simulation method with an analytical solution using the Bloch equation. To demonstrate the feasibility of our proposed method, we compared simulated images with experimental results in which the number of sub-voxels and the amplitude and phase of the target oscillatory magnetic fields varied. In addition, we also applied graphics processing unit parallel computing and investigated the feasibility of avoiding an impracticable calculation time by doing so.

Fast Simulation of Large-Scale Floods Based on GPU Parallel Computing

Computing speed is a significant issue of large-scale flood simulations for real-time response to disaster prevention and mitigation. Even today, most of the large-scale flood simulations are generally run on supercomputers due to the massive amounts of data and computations necessary. In this work, a two-dimensional shallow water model based on an unstructured Godunov-type finite volume scheme was proposed for flood simulation. To realize a fast simulation of large-scale floods on a personal computer, a Graphics Processing Unit (GPU)-based, high-performance computing method using the OpenACC application was adopted to parallelize the shallow water model. An unstructured data management method was presented to control the data transportation between the GPU and CPU (Central Processing Unit) with minimum overhead, and then both computation and data were offloaded from the CPU to the GPU, which exploited the computational capability of the GPU as much as possible. The parallel model was validated using various benchmarks and real-world case studies. The results demonstrate that speed-ups of up to one order of magnitude can be achieved in comparison with the serial model. The proposed parallel model provides a fast and reliable tool with which to quickly assess flood hazards in large-scale areas and, thus, has a bright application prospect for dynamic inundation risk identification and disaster assessment.

Evaluating Hydraulic Properties of Biochar‐Amended Soil Aggregates by High‐Performance Pore‐Scale Simulations

Core Ideas SR‐mCT was used to characterize soil pore structures. Graphics processing unit–enhanced LBM was used to simulate the water flow. Effects of biochar amendment on soil hydraulic properties were evaluated. Biochar amendment can change the soil pore architecture and improve soil physical and hydraulic properties. In this study, two clayey soils were selected and treated with or without biochar amendment (four treatments). Synchrotron‐based X‐ray micro‐computed tomography (SR‐mCT) was used to characterize pore structures of the selected aggregates from the treatments. Based on the high‐resolution soil pore structure information, the three‐dimensional lattice Boltzmann (LB) method was used to simulate water flow in pores. The high‐resolution soil pore structures were also used to define the internal boundary conditions in the LB method that was accelerated by graphics processing unit parallel computing. Results showed that biochar amendment increased soil permeability by at least one order of magnitude, reduced tortuosity by 20 to 30%, and resulted in more uniformly distributed pore water velocities, which was attributed to the change in soil pore geometry. This study is the first physically based modeling effort to assess the effect of biochar amendment on hydraulic properties of soil aggregates. The SR‐mCT method and modeling approach developed in the research play an important role in understanding the mechanisms of water and nutrient transport in soil at the pore scale.

Libration Transfer Design Using Patched Elliptic Three-Body Models and Graphics Processing Units

Design of the transfer from lunar orbits to the sun–Earth libration point region by direct searching in the high-fidelity ephemeris model is an accurate but time-consuming practice. A computationally efficient methodology that takes advantage of the patched elliptic restricted three-body problem model, the power of graphics processing unit parallel computing, and the programming platform of MATLAB is presented. Taking the CHANG’E-2 extension mission as an instance, the proposed implementation obtains almost identical results with that in the ephemeris model and shows significant speedup. Moreover, the methodology can be carried out on inexpensive hardware platforms. Numerical results demonstrate that significant speedups can be achieved using the graphics processing unit parallel computing when compared to solving the same problem on the central processing unit.

Accelerating population balance-Monte Carlo simulation for coagulation dynamics from the Markov jump model, stochastic algorithm and GPU parallel computing

This paper proposes a comprehensive framework for accelerating population balance-Monte Carlo (PBMC) simulation of particle coagulation dynamics. By combining Markov jump model, weighted majorant kernel and GPU (graphics processing unit) parallel computing, a significant gain in computational efficiency is achieved. The Markov jump model constructs a coagulation-rule matrix of differentially-weighted simulation particles, so as to capture the time evolution of particle size distribution with low statistical noise over the full size range and as far as possible to reduce the number of time loopings. Here three coagulation rules are highlighted and it is found that constructing appropriate coagulation rule provides a route to attain the compromise between accuracy and cost of PBMC methods. Further, in order to avoid double looping over all simulation particles when considering the two-particle events (typically, particle coagulation), the weighted majorant kernel is introduced to estimate the maximum coagulation rates being used for acceptance–rejection processes by single-looping over all particles, and meanwhile the mean time-step of coagulation event is estimated by summing the coagulation kernels of rejected and accepted particle pairs. The computational load of these fast differentially-weighted PBMC simulations (based on the Markov jump model) is reduced greatly to be proportional to the number of simulation particles in a zero-dimensional system (single cell). Finally, for a spatially inhomogeneous multi-dimensional (multi-cell) simulation, the proposed fast PBMC is performed in each cell, and multiple cells are parallel processed by multi-cores on a GPU that can implement the massively threaded data-parallel tasks to obtain remarkable speedup ratio (comparing with CPU computation, the speedup ratio of GPU parallel computing is as high as 200 in a case of 100 cells with 10 000 simulation particles per cell). These accelerating approaches of PBMC are demonstrated in a physically realistic Brownian coagulation case. The computational accuracy is validated with benchmark solution of discrete-sectional method. The simulation results show that the comprehensive approach can attain very favorable improvement in cost without sacrificing computational accuracy.

Space-frequency analysis with parallel computing in a phase-sensitive optical time-domain reflectometer distributed sensor.

For a phase-sensitive optical time-domain reflectometer (ϕ-OTDR) distributed sensor system, space-frequency analysis can reduce the false alarm by analyzing the frequency distribution compared with the traditional difference value method. We propose a graphics processing unit (GPU)-based parallel computing method to perform multichannel fast Fourier transform (FFT) and realize the real-time space-frequency analysis. The experiment results show that the time taken by the multichannel FFT decreased considerably based on this GPU parallel computing. The method can be completed with a sensing fiber up to 16 km long and an entry-level GPU. Meanwhile, the GPU can reduce the computing load of the central processing unit from 70% down to less than 20%. We carried out an experiment on a two-point space-frequency analysis, and the results clearly and simultaneously show the vibration point locations and frequency components. The sensor system outputs the real-time space-frequency spectra continuously with a spatial resolution of 16.3 m and frequency resolution of 2.25 Hz.

Efficient GPU parallel computing of population balance—Monte Carlo method for coagulation

The population balance-Monte Carlo (PBMC) method has become increasingly popular because the discrete and stochastic nature of the MCmethod is especially suited for particle dynamics. However, for the two-particle events (typically, particle coagulation), the double looping over all simulation particles is required in normal PBMCmethods, and simulating particle coagulation is in general a challenging computational task due to its numerical complexity and the computing cost. The compute unified device architecture (CUDA) is a programming approach for performing scientific calculations on a graphics processing unit (GPU) as a data-parallel computing device. In this article we present an implementation of accelerating PBMCmethod based on the Inverse scheme and the Acceptance-rejection (AR) scheme for simulating particle coagulation on the GPU. The main idea is to implement the highly threaded data-parallel processing tasks by using GPU and serial computing of complex logic and transaction processing by CPU. Furthermore, the computation accuracy of the PBMC on GPU was validated with a benchmark, a CPU-based discrete-sectional method. To evaluate the accelerating performance, the computing time on the GPU against its sequential counterpart on the CPU was compared. The speedups show that the GPU can accelerate the PBMC by a factor from decades to more than one hundred, depending on the number of simulation particle.

An Improved Image Segmentation Algorithm Based on GPU Parallel Computing

In the process of image segmentation, the classic Fuzzy C-Means (FCM) algorithm is time-consuming and depends heavily on initialization center. Based on Graphic Processing Unit (GPU), this paper proposes a novel FCM algorithm by improving the computational formulas of membership degree and the update criterion of cluster centers. Our algorithm can initialize cluster centers purposefully and further optimize them according to the analysis on the thread model of the graphic hardware. The compared experimental results with the classic FCM algorithm show that our algorithm has obvious superiority in improving image segmentation quality and efficiency.

Accelerating dissipative particle dynamics with graphic processing unit

In this paper, the graphic processing unit (GPU) parallel computing of dissipative particle dynamics (DPD) based on compute unified device architecture is carried out. Some issues involved, such as thread mapping, parallel cell-list array updating, generating pseudo-random number on GPU, memory access optimization and loading balancing are discussed in detail. Furthermore, Poiseuille flow and suddenly contracting and expanding flow are simulated to verify the correctness of GPU parallel computing. The results of GPU parallel computing of DPD show that the speedup ratio is about 20 times compared with central processing unit serial computing.

A Hybrid Optimization Based on GPU Parallel Computing Method and its Application of Three Dimensional Large Eddy Simulations

Using compute Unified device architecture (CUDA), a traditional computational fluid dynamics (CFD) program is paralleled and optimized based on graphic processing unit (GPU). The calculation process is divided into two parts as serial and parallel. Their main characteristics are analyzed and different optimization schemes are given. CPU (central processing unit) and GPU work respectively as flow control and high-speed parallel computation. Bandwidth between devices is applied effectively. Data transfer between devices is moderately improved to simplify algorithm. Finally, the method is verified by simulating a three-dimensional isotropic homogeneous turbulence flow field. The calculation uses large eddy simulation (LES) method with secondary filter and solves the three-dimensional N-S equations. The maximum grid number achieves 8,000,000 and takes 33 seconds each step. All calculations are using ordinary single desktop computer, optimized acceleration ratio can reach 9.

GPU parallel computing: Programming language, debugging tools and data structures

With many cores driven by high memory bandwidth, today’s graphics processing unit (GPU) has involved into an absolute computing workhorse. More and more scientists, researchers and software developers are using GPUs to accelerate their algorithms and applications. Developing complex programs and software on the GPU, however, is still far from easy with existing tools provided by hardware vendors. This article introduces our recent research efforts to make GPU software development much easier. Specifically, we designed BSGP, a high-level programming language for generalpurpose computation on the GPU. A BSGP program looks much the same as a sequential C program, and is thus easy to read, write and maintain. Its performance on the GPU is guaranteed by a well-designed compiler that converts the program to native GPU code. We also developed an effective debugging system for BSGP programs based on the GPU interrupt, a unique feature of BSGP that allows calling CPU functions from inside GPU code. Moreover, using BSGP, we developed GPU algorithms for constructing several widely-used spatial hierarchies for high-performance graphics applications.