Electromagnetic metamaterial simulations using a GPU-accelerated FDTD method

The complex nature of the ocean environment requires advanced computational acoustic models to gain insights into the dominating physical factors controlling the underwater sound propagation and scattering in the ocean. In this study, we explore the “ab-initio” approach to solve wave equations with numerical algorithms that can be implemented in distributed memory parallel computers. The goal is to improve the calculation speed so long-range global scale hydroacoustic wave propagation can be studied more efficiently and effectively. Two major algorithms: the finite difference time domain (FDTD) method and the Parabolic Equation (PE) method are investigated. Two PE-based numerical models are considered. One is the Split-Step-Fourier Parabolic Equation (SSFPE) model using split-step Fourier schemes in three-dimensional (3D) environments. The second PE model is a multi-frequency implementation of the Range-dependent Acoustic Model (RAM) that computes two-dimensional (2D) sound pressure fields. One of the primary challenges in global-scale hydroacoustic propagation modeling with the “ab-initio” approach is the demand for significant computational resources on both memories and computational speeds. To address this, we employed parallel computing techniques using directive-based programming languages, such as OpenACC and XscalableACC, to leverage the power of multiple Graphics Processing Unit (GPU) systems and Central Processing Unit (CPU) cluster computers. Our performance evaluation revealed substantial speedup gains. For the FDTD method, we achieved approximately 3.5-fold and 4-fold speedups with four GPUs and four CPU cluster nodes, respectively. With the 3D SSFPE model, we obtained a speedup of 3.7-fold using four CPU cluster nodes. A significant result was observed with the 2D broadband multi-frequency RAM model, where we achieved a 110-fold speedup compared to one core original implementation. These results demonstrate the promising potential of massively parallel computers for ocean acoustic propagation modeling and highlight the significant performance benefits of utilizing parallel computing techniques. Our findings emphasize the importance of efficient computational strategies.

The finite-difference time-domain (FDTD) algorithm has become a tool of choice in many areas of RF and microwave engineering and optics. However, FDTD runs too slow for some simulations to be practical, even when carried out on supercomputers. The development of dedicated hardware to accelerate FDTD computations has been investigated. In this paper, we demonstrate that off-the-shelf graphics processor units (GPUs) can be successfully used to accelerate FDTD simulations. Using C++, OpenGL, and several OpenGL extensions, a modern GPU has been programmed to solve a simple two dimensional electromagnetic scattering problem. The GPU outperforms a central processing unit (CPU) of comparable technology generation.

Metal probe-based near-field tweezers can provide optical trapping and alignment of dielectric particles at nanometer scale. A finite difference time domain (FDTD) numerical method of solution is applied to optimize the metal probe geometry for better nano-manipulation. Calculations for copper probes of truncated, infinite and finite rectangular pyramid are considered. The calculations show that sharper probes would generate stronger fields, and probes whose geometry size matched to the excitation frequency could lead to higher field enhancement, in addition, finite probes might generate strong field enhancement due to resonance with the excitation source. Consequently, FDTD calculations were made to optimize metal probes in the near-infrared regime, the enhancements for cone probes are found to be higher than for similar length pyramidal probes. The cone probes designed with FDTD are particularly well suited for use in metal probe-based near-field tweezers.

Tuning of InGaAsP planar photonic crystal nanocavities by local liquid crystal infiltration

The use of Graphics Processing Unit (GPU) for computational work has revolutionized how complex electromagnetic problems are solved. Complex problems which required supercomputers in the past for analysis can now be tackled and solved using personal computers by channeling the computational work towards GPUs instead of the traditional computer Central Processing Unit (CPU). Finite-Difference Time-Domain (FDTD) analysis, which is a computationally expensive method of solving electromagnetic problems is highly parallel in nature and can be readily executed in a GPU. We have developed an algorithm for three dimensional FDTD analysis of optical devices with micro and nano-structures using Compute Unified Device Architecture (CUDA). The developed algorithm exploits the benefits of multiple cores of GPU chips and boosts the speed of simulation without sacrificing its accuracy. We achieved a 25-fold speed up of simulation using CUDA compared to MATLAB code in CPU.

In order to determine the signal attenuation that might be expected when using a portable telephone inside an automobile, the signal attenuation for a variety of incidence angles and antenna locations inside a car with one or two passengers was measured at 1920 MHz. In addition to the measurements, finite difference time domain (FDTD) calculations of the signal attenuation were also made. The FDTD calculations and measured signal attenuation were compared, and agreed well with one another. The FDTD predictions were then analyzed to provide statistical summaries of the signal attenuation characteristics for different incidence angles, number of passengers, windows up/down, and receiving antenna locations and orientations.

In this paper, a full wave finite difference time domain (FDTD) computation method based on cylindrical coordinate system is proposed for calculating the resonant frequency of circular cavity resonator. The use of FDTD method with cylindrical coordinate system instead of Cartesian coordinate system is considered due to the geometry shape of analyzed structure, i.e. circular cavity resonator. In the computation, a hollow circular cavity resonator made of perfect conductor with the radius of 50mm and length of 100mm is discretized based on cylindrical coordinate system and numerically computed to determine its resonant frequencies. To verify the proposed method, the theoretical approach for the cavity is carried out by calculating its resonant frequencies for correspondent resonance mode. In addition, a simulation for determining resonant frequencies of the cavity is also conducted using finite element method (FEM) commercialized software. It shows that the FDTD computation result demonstrates an acceptable accuracy compared to the theoretical approach with the discrepancy less than 5% although it is worse than the result of FEM commercialized software.

New version of FDTD with GFM program in Schwarzschild space–time

SummaryMany e‐business or social network servers have been constructed on cloud. On such open environments, private data of massive users have to be protected by encrypting, such as using Advanced Encryption Standard (AES), and furthermore, this process must be finished in a short time for users' better experience. This gives huge pressure on cloud servers, especially common servers, such as web servers. We urgently need an inexpensive and highly efficient method to relieve cloud servers' pressure. Fortunately, many cores of a graphics processing unit (GPU) can undertake this hard mission because of stronger computing power and lower price. The GPU environments can be virtualized on demand by cloud through the vCUDA technology. Of course, for those clouds not equipped with a GPU, a central processing unit (CPU) can still work as multithreads in parallel. Thus, in a cloud, AES can be parallelized using many cores of a GPU or multicores of a CPU with high efficiency and low cost. For typical cloud applications, such as web services, there are massive users and each one has short plaintext. If we simply parallelize AES in such an application, we cannot obtain better performance because of the GPU's extra data transferring cost. Thus, we coalesce the massive users' data and cut these data into same‐length slices for improving the performance of parallel AES as much as possible. So we design six parallel AES algorithms using GPU parallelism or CPU parallelism, which differ in parallel scope and whether data are coalesced or cut to slices. Specifically, they are coalescent and sliced GPU (GCS), coalescent and unsliced GPU, uncoalescent GPU, coalescent and sliced CPU, coalescent and unsliced CPU, and uncoalescent CPU. Moreover, we implement them on two representative platforms and evaluate their performance. Through comparing their performance, GCS has the best performance among these algorithms. In a cloud with Nvidia GPUs, GCS is a more powerful algorithm for massive users' data encrypting, relatively. Copyright © 2015 John Wiley & Sons, Ltd.

Efficient methods for implementation of multi-level nonrigid mass-preserving image registration on GPUs and multi-threaded CPUs

We have implemented the Martini force field within Lawrence Livermore National Laboratory's molecular dynamics program, ddcMD. The program is extended to a heterogeneous programming model so that it can exploit graphics processing unit (GPU) accelerators. In addition to the Martini force field being ported to the GPU, the entire integration step, including thermostat, barostat, and constraint solver, is ported as well, which speeds up the simulations to 278-fold using one GPU vs one central processing unit (CPU) core. A benchmark study is performed with several test cases, comparing ddcMD and GROMACS Martini simulations. The average performance of ddcMD for a protein-lipid simulation system of 136k particles achieves 1.04 µs/day on one NVIDIA V100 GPU and aggregates 6.19 µs/day on one Summit node with six GPUs. The GPU implementation in ddcMD offloads all computations to the GPU and only requires one CPU core per simulation to manage the inputs and outputs, freeing up remaining CPU resources on the compute node for alternative tasks often required in complex simulation campaigns. The ddcMD code has been made open source and is available on GitHub at https://github.com/LLNL/ddcMD.

The numerical dispersion is one of the main factors to affect the accuracy of the finite-difference time-domain (FDTD) method. It can be easy to be taken for granted that a smaller time step leads to smaller simulation errors. This paper reveals that smaller time steps do not always make more accurate results in FDTD simulations. We analytically investigated how time steps affect the numerical dispersion of two FDTD methods: the leapfrog FDTD(2,2) method and the FDTD(2,4) method. The investigations in this paper show that in the FDTD(2,2) method, a larger time step limited by the Courant-Friedrichs-Lewy (CFL) condition is more helpful to reduce the numerical dispersion error. However, in the FDTD(2,4) method, as the time step grows, the numerical dispersion error decreases at the beginning and then increases. Several numerical examples are carried out to verify our analysis.

MSA Probs is a parallel algorithm developed to align multiple sequence alignment using a central processing unit (CPU). Whereas the CPU has some limitations, such as the inability to parallelize tasks in the processor (latency-oriented). To overcome these limitations, this paper proposes an improved version of MSA Probs that is compatible with a graphical Processing Unit (GPU). This idea helps in enhancing the performance of our algorithm (MSAprobs). To parallelize the sequential algorithm, Compute Unified Device Architecture (CUDA) or OpenCL is commonly used on GPUs. The NIVIDIA API is used to investigate the GPU's computing power. The results of using CPU only in MSAprobs versus the CPU and GPU are compared using two data sets from the Bali Base and OX Bench. The evaluation of the CPU and GPU is done using Threads 1,2 and 4. The results showed that by combining CPU and GPU, performance is improved and execution time is reduced.

This study aims to employ both central processing units (CPUs) and graphics processing units (GPUs) to collaboratively generate contour lines from a large-scale digital elevation model (DEM). The performance was improved with regard to three aspects. First, the original DEM data were decomposed and assigned according to the GPU’s limited memory so that large-scale data could be correctly addressed. Second, different types of computational tasks between the CPUs and GPUs were dynamically scheduled to ensure that both accelerators cooperate for performance improvement. Third, parallel processing on GPUs and CPUs was separately optimized for more efficient acceleration. Experimental results indicated that applying the parallel algorithm to data with a volume of 37.81 GB and area of 5,975,625.16 km2 reduced the total execution time from 332.84 min to 8.29 min for an optimal speedup of 40.15. In addition, we investigated the effects of the computational intensity, decomposition granularity, and task scheduling on parallel efficiency and performance improvement. We also discussed its degree of effectiveness, broader application, and the future direction of research in the field of geographic information systems.

Artificial neural networks (ANNs) have appeared as a potential alternative for uncertainty quantification (UQ) in the finite difference time-domain (FDTD) computation. They are applied to build a surrogate model for the computation-intensive FDTD simulation and to bypass the numerous simulations required for UQ. However, when the surrogate model utilizes an ANN, a considerable number of data are generally required for high accuracy, and generating such large quantities of data becomes computationally prohibitive. To address this drawback, a number of adaptations for ANNs are proposed, which additionally improves the accuracy of ANNs in UQ for the FDTD computation while maintaining a low computational cost. The proposed algorithm is tested for application in bioelectromagnetics, and considerable speed up, as well as the improved accuracy of UQ, is observed compared to traditional methods such as the nonintrusive polynomial chaos (NIPC) method.