Real-Time Wavefront Measurement Based on GPU-Accelerated Parallel Algorithm Using Phase Diversity Images

Multi-core platform enters the territory of high performance computing (HPC). Moreover, the NVIDA GT200 has 240 cores and performs thousands upon thousands of threads simultaneously. The role of the Graphics Processing Units (GPU)accelerator has become more and more important for scientific computing and computational fluid dynamic (CFD) to obtain result quickly and efficiently. In this paper we presented efficient computational scheme for solving parabolic partial differential equations on Multithreading and GPU accelerator. The purpose of this paper is to obtain the solutions as accurate and high speed-up using implicit scheme is convection-diffusion-reaction(CDR). In this paper we propose to CUDA-CDR solver to quickly solve big problem on GPU accelerator. The NVIDIA CUDATM is an extension of C language and has been implemented as a veteran environment since 2006. Our multithread version illustrates 2.5 times faster than those on a sequential code in Model Problem 1 with the problem size of 400x400. The performance of parallel version via CUDA C is 11 times faster than those on a sequential code in Model Problem 1 with the problem size of 400x400.

GPU acceleration of conjugate gradient method obtaining Green's function for transport-property calculation

With the development of information technology and the increasing demand for data processing, the serial mode of the central processing unit (CPU) is difficult to efficiently transmit large-scale spatiotemporal data, and the processing effect for high-resolution images is not good. This paper designed a high-resolution image processing and spatiotemporal data transmission system based on graphics processing unit (GPU) acceleration to improve the processing efficiency of large-scale spatiotemporal data. In this paper, traffic spatiotemporal data was taken as an example for analysis. Large-scale traffic image data was collected by road monitoring equipment, and image compression was performed on the collected image. Fourier transform was used to eliminate image data redundancy, and GPU-accelerated parallel processing was used to achieve fast image defogging and data transmission. This paper selected 2TB of traffic spatiotemporal data with image resolutions of 540P, 720P, 1080P, 1440P, and 2160P. GPU acceleration was performed using the Compute Unified Device Architecture (CUDA). In images with a resolution of 2160P, the processing time for CPU and GPU acceleration was 2900ms and 28ms, respectively, with an acceleration ratio of 103.6. A high-resolution image processing and spatiotemporal data transmission system based on GPU acceleration can improve the efficiency of traffic spatiotemporal data processing and have excellent concurrent processing capabilities.

<strong class="journal-contentHeaderColor">Abstract.</strong> With semiconductor technology gradually approaching its physical and thermal limits, Graphics processing unit (GPU) is becoming an attractive solution in many scientific applications due to their high performance. This paper presents an application of GPU accelerators in air quality model. We endeavor to demonstrate an approach that runs a PPM solver of horizontal advection (HADVPPM) for air quality model CAMx on GPU clusters. Specifically, we first convert the HADVPPM to a new Compute Unified Device Architecture C (CUDA C) code to make it computable on the GPU (GPU-HADVPPM). Then, a series of optimization measures are taken, including reducing the CPU-GPU communication frequency, increasing the size of data computation on GPU, optimizing the GPU memory access, and using thread and block indices in order to improve the overall computing performance of CAMx model coupled with GPU-HADVPPM (named as CAMx-CUDA model). Finally, a heterogeneous, hybrid programming paradigm is presented and utilized with the GPU-HADVPPM on GPU clusters with Massage Passing Interface (MPI) and CUDA. Offline experiment results show that running GPU-HADVPPM on one NVIDIA Tesla K40m and NVIDIA Tesla V100 GPU can achieve up to 845.4x and 1113.6x acceleration. By implementing a series of optimization schemes, the CAMx-CUDA model resulted in a 29.0x and 128.4x improvement in computational efficiency using a GPU accelerator card on a K40m and V100 cluster, respectively. In terms of the single-module computational efficiency of GPU-HADVPPM, it can achieve 1.3x and 19.4x speedup on NVIDIA Tesla K40m GPU and NVIDA Tesla V100 GPU respectively. The multi-GPU acceleration algorithm enables 4.5x speedup with 8 CPU cores and 8 GPU accelerators on V100 cluster.

<strong class="journal-contentHeaderColor">Abstract.</strong> With semiconductor technology gradually approaching its physical and thermal limits, Graphics processing unit (GPU) is becoming an attractive solution in many scientific applications due to their high performance. This paper presents an application of GPU accelerators in air quality model. We endeavor to demonstrate an approach that runs a PPM solver of horizontal advection (HADVPPM) for air quality model CAMx on GPU clusters. Specifically, we first convert the HADVPPM to a new Compute Unified Device Architecture C (CUDA C) code to make it computable on the GPU (GPU-HADVPPM). Then, a series of optimization measures are taken, including reducing the CPU-GPU communication frequency, increasing the size of data computation on GPU, optimizing the GPU memory access, and using thread and block indices in order to improve the overall computing performance of CAMx model coupled with GPU-HADVPPM (named as CAMx-CUDA model). Finally, a heterogeneous, hybrid programming paradigm is presented and utilized with the GPU-HADVPPM on GPU clusters with Massage Passing Interface (MPI) and CUDA. Offline experiment results show that running GPU-HADVPPM on one NVIDIA Tesla K40m and NVIDIA Tesla V100 GPU can achieve up to 845.4x and 1113.6x acceleration. By implementing a series of optimization schemes, the CAMx-CUDA model resulted in a 29.0x and 128.4x improvement in computational efficiency using a GPU accelerator card on a K40m and V100 cluster, respectively. In terms of the single-module computational efficiency of GPU-HADVPPM, it can achieve 1.3x and 19.4x speedup on NVIDIA Tesla K40m GPU and NVIDA Tesla V100 GPU respectively. The multi-GPU acceleration algorithm enables 4.5x speedup with 8 CPU cores and 8 GPU accelerators on V100 cluster.

The energetic fuel-coolant interaction (FCI) has been one of the primary safety concerns in nuclear power plants. Graphical processing unit (GPU) implementation of the moving particle semi-implicit (MPS) method is presented and used to simulate the fuel coolant interaction problem. The governing equations are discretized with the particle interaction model of MPS. Detailed implementation on single-GPU is introduced. The three-dimensional broken dam is simulated to verify the developed GPU acceleration MPS method. The proposed GPU acceleration algorithm and developed code are then used to simulate the FCI problem. As a summary of results, the developed GPU-MPS method showed a good agreement with the experimental observation and theoretical prediction.

GPU acceleration of MPAS microphysics WSM6 using OpenACC directives: Performance and verification

Most Computational Fluid Dynamics (CFD) applications solve the Naiver-Stokes equations using various discretization methods like Finite Difference Method (FDM), Finite Element Method (FEM), Finite Volume Method (FVM), etc. The most compute-intensive process in CFD algorithms is the pressure Poisson solver. Solving the pressure Poisson equation requires the solution of a set of linear simultaneous equations. As the problem size/complexity increases, so does the effort and time invested in solving the equations. This study presents fast and robust iterative solvers and the acceleration of the same using the Graphics Processing Unit (GPU) architecture. Serial versions of the codes are implemented in the C language and the parallelization on GPU is achieved using Compute Unified Device Architecture (CUDA). For this study, a two-dimensional heat conduction problem is considered. FDM is used for discretization. Iterative solvers Conjugate Gradient and Multi Grid methods have been implemented in serial and parallel. The performance enhancement with single GPU and CPU system over a single CPU in terms of computing time is reported. It is seen that multigrid algorithms are superior in convergence and nearly 13 times speed-up has been obtained through GPU acceleration using CUDA, for a grid size of 2048 X 2048. The tests have been done on a cluster with Intel(R) Xeon(R) CPU E5-2670 CPU and NVIDIA K10 GPU.

Intelligent optimization algorithms such as particle swarm optimization (PSO) have been introduced into four-dimensional variational assimilation of atmospheric data to solve complex optimization problems. The time-varying double compression model can solve the problem of accuracy well. But when confronted with the problem of high accuracy, the long training time will become the weakness. Parallelization acceleration is one of the effective ways to solve the conundrum. And applying Graphic Processing Unit (GPU) to accelerate PSO algorithm in parallel has the advantage of low hardware cost. In this paper, a parallel time-varying double compression factor PSO algorithm based on GPU acceleration is proposed. The parallel operation of particle swarm optimization algorithm is carried out by GPU, in which the time can be improved with the same precision kept. Compared with the dynamic inertia weight algorithm and time-varying double compression factor algorithm, the experimental results display that the accuracy is better than the former and the consuming time is shorter than the latter, which proves that the method can process the prediction in a faster and more accurate way.KeywordsData assimilationGraphic Processing UnitTime-varying double compression factor PSO algorithmWeather forecast

Robotic manipulation is a challenging research area, particularly in developing policies that generalize across diverse real-world scenarios. While real-world data can be slow and unsafe, simulations offer a safer and faster alternative. However, training in simulations can still be time-consuming, hindering rapid model iteration. This paper explores using graphics processing unit (GPU) acceleration to speed up training for robotic manipulation tasks in simulations. By comparing GPU and CPU performance, we demonstrate a significant reduction in training time. The findings show that GPU hardware enhances policy development efficiency, accelerating research and applications, including sim-to-real transfer. Additionally, it broadens exploration of state and action spaces, providing agents with a diverse range of training experiences. A simulation benchmark was also created to test GPU acceleration, detailing task selection, environment setup, and performance measurement. This benchmark forms the basis for evaluating the speedup achieved by GPUs in training robotic manipulation models.

The graphic processing unit (GPU) acceleration of 3D eddy current losses calculation by parallelizing the element by element (EbE) finite element (FE) method is proposed in the paper. Two distinctive characteristics of EbE method included, one is not to assemble the system matrix and the other is inherent parallel nature. So we can fulfill the parallelism directly on the elements level based on the GPU platform. Considering the ill-conditioned character of 3D eddy current problem, a novel preconditioner named generalized Jacobi (GJ) is used to accelerate the conjugate gradient (CG) iterative solution scheme. A large Transformer is taken as an example to compute the eddy current losses to validate the proposed method, the numerical experiments is carried out on the GPU.

GPU acceleration for general conservation equations and its application to several engineering problems

Chapter 1 - GPU-Accelerated Computation and Interactive Display of Molecular Orbitals

A novel block-structured Cartesian mesh method is developed that is well designed for the graphics processing unit (GPU) acceleration of flow simulations. The size of the mesh block is set based on the thread hierarchy in Compute Unified Device Architecture which is an easy-to-use GPU programming model. The mesh method is implemented in a finite-volume compressible flow solver, where the two-dimension steady Euler equations are solved by using the AUSM + scheme in spatial discretization and third-order total-variation-diminishing Runge–Kutta method in time discretization. For the GPU implementation, the redundant computation, data structure reorganization and Structure-of-Arrays (SoA) mesh layout are utilized to reduce the frequency and data size for information transfer between host and device. Two test cases, the supersonic flow over a circular cylinder and a NACA0012 airfoil, are presented to validate numerical approaches and valuate computational performance. Additionally, numerical experiments are carried out on a test case at different mesh sizes and execution configurations to investigate properties of the proposed method.

Due to the scarcity and high cost of satellite frequency spectrum, it is very important to utilize the available spectrum as efficiently as possible. The efficient usage of the spectrum in the satellite return link is a challenging task, especially if multiple users are present. In previous works Multi-User Detection (MUD) techniques have been widely studied to increase the spectral efficiency of the satellite return link. However, due to the high computational complexity and its sensitivity to synchronization and channel estimation errors, only few implementations of MUD for satellite communications exist. In this paper a novel Graphics Processing Unit (GPU)-based Software Defined Radio (SDR) implementation of a MUD receiver for transparent satellite return link is presented, which uses iterative channel estimation and decoding. The implementation can cope with the presence of strong phase noise. In addition to its high flexibility and low cost, with the GPU acceleration our SDR MUD receiver implementation achieves a decoding throughput of 270 Kbps using a single GPU card.