A Fast Runtime Visualization of a GPU-Based 3D-FDTD Electromagnetic Simulation

In this paper, we present design and implementation of a fast runtime visualizer for a GPU-based 3D-FDTD electromagnetic simulation. We focus on improving the productivity of simulator development without compromising simulation performance. In order to keep the portability, we implemented a visualizer with the MVC model, where simulation kernels and visualization process were completely separated. For high-speed visualization, an interoperability mechanism between OpenGL and CUDA was used in addition to efficient utilization of programmable shaders. We also propose an asynchronous multi-threaded execution with a triple-buffering technique so that developers can concentrate on developing their simulation kernels. As a result of empirical visualization experiments of electromagnetic simulations for practical antenna design, it was revealed that our implementation achieved a rendering throughput of 90 FPS for a view port of 512 x 512 pixels, which corresponds to a 12.9 times speedup compared to when the OpenGL-CUDA interoperability mechanism was not utilized. When a standard visualization throughput of 60 FPS was selected, the performance overhead imposed by the visualization process was 15.8%, which was reasonably low compared to a speedup of the simulation kernel gained by the GPU acceleration

Graphics Processing Unit Acceleration Of Computational Electromagnetic Methods

The use of Graphical Processing Units (GPU\u27s) for scientific applications has been evolving and expanding for the decade. GPU\u27s provide an alternative to the CPU in the creation and execution of the numerical codes that are often relied upon in to perform simulations in computational electromagnetics. While originally designed purely to display graphics on the users monitor, GPU\u27s today are essentially powerful floating point co-processors that can be programmed not only to render complex graphics, but also perform the complex mathematical calculations often encountered in scientific computing. Currently the GPU\u27s being produced often contain hundreds of separate cores able to access large amounts of high-speed dedicated memory. By utilizing the power offered by such a specialized processor, it is possible to drastically speed up the calculations required in computational electromagnetics. This increase in speed allows for the use of GPU based simulations in a variety of situations that the computational time has heretofore been a limiting factor in, such as in educational courses. Many situations in teaching electromagnetics often rely upon simple examples of problems due to the simulation times needed to analyze more complex problems. The use of GPU based simulations will be shown to allow demonstrations of more advanced problems than previously alloby adapting the methods for use on the GPU. Modules will be developed for a wide variety of teaching situations utilizing the speed of the GPU to demonstrate various techniques and ideas previously unrealizable

Lightning Modeling and Its Effects on Electric Infrastructures

When it comes to dealing with high voltages or issues of high electric currents, infrastructure security and people’s safety are of paramount importance. These kinds of phenomena have dangerous consequences, therefore studies concerning the effects of lightning are crucial. The normal operation of transmission and distribution systems is greatly affected by lightning, which is one of the major causes of power interruptions: direct or nearby indirect strikes can cause flashovers in overhead transmission and distribution lines, resulting in over voltages on the line conductors. Contributions to this Special Issue have mainly focused on modelling lightning activity, investigating physical causes, and discussing and testing mathematical models for the electromagnetic fields associated with lighting phenomena. In this framework, two main topics have emerged: 1) the interaction between lightning phenomena and electrical infrastructures, such as wind turbines and overhead lines; and 2) the computation of lightning electromagnetic fields in the case of particular configuration, considering a negatively charged artificial thunderstorm or considering a complex terrain with arbitrary topograph

Efficient Techniques for Wave-based Sound Propagation in Interactive Applications

Sound propagation techniques model the effect of the environment on sound waves and predict their behavior from point of emission at the source to the final point of arrival at the listener. Sound is a pressure wave produced by mechanical vibration of a surface that propagates through a medium such as air or water, and the problem of sound propagation can be formulated mathematically as a second-order partial differential equation called the wave equation. Accurate techniques based on solving the wave equation, also called the wave-based techniques, are too expensive computationally and memory-wise. Therefore, these techniques face many challenges in terms of their applicability in interactive applications including sound propagation in large environments, time-varying source and listener directivity, and high simulation cost for mid-frequencies. In this dissertation, we propose a set of efficient wave-based sound propagation techniques that solve these three challenges and enable the use of wave-based sound propagation in interactive applications. Firstly, we propose a novel equivalent source technique for interactive wave-based sound propagation in large scenes spanning hundreds of meters. It is based on the equivalent source theory used for solving radiation and scattering problems in acoustics and electromagnetics. Instead of using a volumetric or surface-based approach, this technique takes an object-centric approach to sound propagation. The proposed equivalent source technique generates realistic acoustic effects and takes orders of magnitude less runtime memory compared to prior wave-based techniques. Secondly, we present an efficient framework for handling time-varying source and listener directivity for interactive wave-based sound propagation. The source directivity is represented as a linear combination of elementary spherical harmonic sources. This spherical harmonic-based representation of source directivity can support analytical, data-driven, rotating or time-varying directivity function at runtime. Unlike previous approaches, the listener directivity approach can be used to compute spatial audio (3D audio) for a moving, rotating listener at interactive rates. Lastly, we propose an efficient GPU-based time-domain solver for the wave equation that enables wave simulation up to the mid-frequency range in tens of minutes on a desktop computer. It is demonstrated that by carefully mapping all the components of the wave simulator to match the parallel processing capabilities of the graphics processors, significant improvement in performance can be achieved compared to the CPU-based simulators, while maintaining numerical accuracy. We validate these techniques with offline numerical simulations and measured data recorded in an outdoor scene. We present results of preliminary user evaluations conducted to study the impact of these techniques on user's immersion in virtual environment. We have integrated these techniques with the Half-Life 2 game engine, Oculus Rift head-mounted display, and Xbox game controller to enable users to experience high-quality acoustics effects and spatial audio in the virtual environment.Doctor of Philosoph

Interactive physically-based sound simulation

The realization of interactive, immersive virtual worlds requires the ability to present a realistic audio experience that convincingly compliments their visual rendering. Physical simulation is a natural way to achieve such realism, enabling deeply immersive virtual worlds. However, physically-based sound simulation is very computationally expensive owing to the high-frequency, transient oscillations underlying audible sounds. The increasing computational power of desktop computers has served to reduce the gap between required and available computation, and it has become possible to bridge this gap further by using a combination of algorithmic improvements that exploit the physical, as well as perceptual properties of audible sounds. My thesis is a step in this direction. My dissertation concentrates on developing real-time techniques for both sub-problems of sound simulation: synthesis and propagation. Sound synthesis is concerned with generating the sounds produced by objects due to elastic surface vibrations upon interaction with the environment, such as collisions. I present novel techniques that exploit human auditory perception to simulate scenes with hundreds of sounding objects undergoing impact and rolling in real time. Sound propagation is the complementary problem of modeling the high-order scattering and diffraction of sound in an environment as it travels from source to listener. I discuss my work on a novel numerical acoustic simulator (ARD) that is hundred times faster and consumes ten times less memory than a high-accuracy finite-difference technique, allowing acoustic simulations on previously intractable spaces, such as a cathedral, on a desktop computer. Lastly, I present my work on interactive sound propagation that leverages my ARD simulator to render the acoustics of arbitrary static scenes for multiple moving sources and listener in real time, while accounting for scene-dependent effects such as low-pass filtering and smooth attenuation behind obstructions, reverberation, scattering from complex geometry and sound focusing. This is enabled by a novel compact representation that takes a thousand times less memory than a direct scheme, thus reducing memory footprints to within available main memory. To the best of my knowledge, this is the only technique and system in existence to demonstrate auralization of physical wave-based effects in real-time on large, complex 3D scenes

FDTD Simulation Techniques for Simulation of Very Large 2D and 3D Domains Applied to Radar Propagation over the Ocean

abstract: A domain decomposition method for analyzing very large FDTD domains, hundreds of thousands of wavelengths long, is demonstrated by application to the problem of radar scattering in the maritime environment. Success depends on the elimination of artificial scattering from the “sky” boundary and is ensured by an ultra-high-performance absorbing termination which eliminates this reflection at angles of incidence as shallow as 0.03 degrees off grazing. The two-dimensional (2D) problem is used to detail the features of the method. The results are cross-validated by comparison to a parabolic equation (PE) method and surface integral equation method on a 1.7km sea surface problem, and to a PE method on propagation through an inhomogeneous atmosphere in a 4km-long space, both at X-band. Additional comparisons are made against boundary integral equation and PE methods from the literature in a 3.6km space containing an inhomogeneous atmosphere above a flat sea at S-band. The applicability of the method to the three-dimensional (3D) problem is shown via comparison of a 2D solution to the 3D solution of a corridor of sea. As a technical proof of the scalability of the problem with computational power, a 5m-wide, 2m-tall, 1050m-long 3D corridor containing 321.8 billion FDTD cells has been simulated at X-band. A plane wave spectrum analysis of the (X-band) scattered fields produced by a 5m-wide, 225m-long realistic 3D sea surface, and the 2D analog surface obtained by extruding a 2D sea along the width of the corridor, reveals the existence of out-of-plane 3D phenomena missed by the traditional 2D analysis. The realistic sea introduces random strong flashes and nulls in addition to a significant amount of cross-polarized field. Spatial integration using a dispersion-corrected Green function is used to reconstruct the scattered fields outside of the computational FDTD space which would impinge on a 3D target at the end of the corridor. The proposed final approach is a hybrid method where 2D FDTD carries the signal for the first tens of kilometers and the last kilometer is analyzed in 3D.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

Microwave Tomography Using Stochastic Optimization And High Performance Computing

This thesis discusses the application of parallel computing in microwave tomography for detection and imaging of dielectric objects. The main focus is on microwave tomography with the use of a parallelized Finite Difference Time Domain (FDTD) forward solver in conjunction with non-linear stochastic optimization based inverse solvers. Because such solvers require very heavy computation, their investigation has been limited in favour of deterministic inverse solvers that make use of assumptions and approximations of the imaging target. Without the use of linearization assumptions, a non-linear stochastic microwave tomography system is able to resolve targets of arbitrary permittivity contrast profiles while avoiding convergence to local minima of the microwave tomography optimization space. This work is focused on ameliorating this computational load with the use of heavy parallelization. The presented microwave tomography system is capable of modelling complex, heterogeneous, and dispersive media using the Debye model. A detailed explanation of the dispersive FDTD is presented herein. The system uses scattered field data due to multiple excitation angles, frequencies, and observation angles in order to improve target resolution, reduce the ill-posedness of the microwave tomography inverse problem, and improve the accuracy of the complex permittivity profile of the imaging target. The FDTD forward solver is parallelized with the use of the Common Unified Device Architecture (CUDA) programming model developed by NVIDIA corporation. In the forward solver, the time stepping of the fields are computed on a Graphics Processing Unit (GPU). In addition the inverse solver makes use of the Message Passing Interface (MPI) system to distribute computation across multiple work stations. The FDTD method was chosen due to its ease of parallelization using GPU computing, in addition to its ability to simulate wideband excitation signals during a single forward simulation. We investigated the use of distributed Particle Swarm Optimization (PSO) and Differential Evolution (DE) methods in the inverse solver for this microwave tomography system. In these optimization algorithms, candidate solutions are farmed out to separate workstations to be evaluated. As fitness evaluations are returned asynchronously, the optimization algorithm updates the population of candidate solutions and gives new candidate solutions to be evaluated to open workstations. In this manner, we used a total of eight graphics processing units during optimization with minimal downtime. Presented in this thesis is a microwave tomography algorithm that does not rely on linearization assumptions, capable of imaging a target in a reasonable amount of time for clinical applications. The proposed algorithm was tested using numerical phantoms that with material parameters similar to what one would find in normal or malignant human tissue

Accelerating the Performance of a Novel Meshless Method Based on Collocation With Radial Basis Functions By Employing a Graphical Processing Unit as a Parallel Coprocessor

In recent times, a variety of industries, applications and numerical methods including the meshless method have enjoyed a great deal of success by utilizing the graphical processing unit (GPU) as a parallel coprocessor. These benefits often include performance improvement over the previous implementations. Furthermore, applications running on graphics processors enjoy superior performance per dollar and performance per watt than implementations built exclusively on traditional central processing technologies. The GPU was originally designed for graphics acceleration but the modern GPU, known as the General Purpose Graphical Processing Unit (GPGPU) can be used for scientific and engineering calculations. The GPGPU consists of massively parallel array of integer and floating point processors. There are typically hundreds of processors per graphics card with dedicated high-speed memory. This work describes an application written by the author, titled GaussianRBF to show the implementation and results of a novel meshless method that in-cooperates the collocation of the Gaussian radial basis function by utilizing the GPU as a parallel co-processor. Key phases of the proposed meshless method have been executed on the GPU using the NVIDIA CUDA software development kit. Especially, the matrix fill and solution phases have been carried out on the GPU, along with some post processing. This approach resulted in a decreased processing time compared to similar algorithm implemented on the CPU while maintaining the same accuracy

SMUTHI: A python package for the simulation of light scattering by multiple particles near or between planar interfaces

SMUTHI is a python package for the efficient and accurate simulation of electromagnetic scattering by one or multiple wavelength-scale objects in a planarly layered medium. The software combines the T-matrix method for individual particle scattering with the scattering matrix formalism for the propagation of the electromagnetic field through the planar interfaces. In this article, we briefly introduce the relevant theoretical concepts and present the main features of SMUTHI. Simulation results obtained for several benchmark configurations are validated against commercial software solutions. Owing to the generality of planarly layered geometries and the availability of different particle shapes and light sources, possible applications of SMUTHI include the study of discrete random media, meta-surfaces, photonic crystals and glasses, perforated membranes and plasmonic systems, to name a few relevant examples at visible and near-visible wavelengths

Best practices for building hardware designs for living computational science applications

Scientific computing or Computational science, is a field of study where engineers and scientists use computer simulations to solve equations that model the physical world. In some cases, these equations come from the first principles of physics. In the past, these simulations were run on a single processor machine. However, due to various technological reasons, the performance of these machines are not likely to improve at the same rate as in the past. In order to improve the performance per watt of these simulations, special-purpose hardware accelerators can be used. This work mainly focuses on using FPGA-based hardware accelerators. In order to run these simulations on an FPGA accelerator, the application code needs to be re-factored into software and hardware sections. These faster simulations have motivated scientists to capture more behavior of the physical world. As additional behavior is captured, the application code needs to be re-factored each time, and a significant effort is required to re-build the design. Unfortunately, these multiple cycles of re-design reduces the overall productivity of scientists and engineers. This work proposes a set of hardware design guidelines for changing computational science codes or living computational science codes. These guidelines co-evolve the hardware with the software, reducing the overall effort of re-design and improving productivity. The design guidelines are evaluated for effectiveness, communicability, and broad applicability. Experimental results have shown that the overall re-design effort is reduced, and these guidelines are broadly applicable to a wide variety of scientific computing applications