Parallel computing method for generating hologram of 3D objects using layer-based method

The application of parallel computers to reservoir simulation is studied with the aid of two recently introduced Multiple Instruction, Multiple Data (MIMD) computers. The problems posed by reservoir simulators are shown to be particularly well suited for such parallel computers* Fundamental parallel programming techniques using Fortran are presented for the Hetrogeneous Element Processor (HEP) produced by Denelcor and the iPSC Hypercube produced by Intel. These techniques are then applied to two of the most time consuming tasks in reservoir simulation: forming the matrix coefficients and producing a sparse matrix solution. The problem of parallel formation of sparse matrix coefficients is addressed for the single and multiphase cases using both black oil and compositional fluid models. Parallel sparse matrix solution is addressed by considering D4 ordered Gaussian elimination, and the multigrid, conjugate gradient, and Successive Over-Relaxation (SOR) methods. Three parallel algorithms based on SOR are developed to illustrate the possible diversity of parallel algorithms. The red-black and multicolored SOR methods which are often used on vector machines are shown to be easily adaptable to parallel MIMD machine. Matrix partitioning and a new method of iteration pipelining are also presented as parallel SOR methods. The various SOR algorithms, in parallel form, are mapped onto the HEP to illustrate the speedups currently possible on an MIMD machine. Projections are made on the future importance of these types of machines to reservoir simulation.

Traditionally, topics such as parallel computing, computer graphics, and artificial intelligence have been taught as stand-alone courses in the computing curriculum. Often these are elective courses, limiting the material to the subset of students choosing to take the course. Recently there has been movement to distribute topics across the curriculum in order to ensure that all graduates have been exposed to concepts such as parallel computing. This work describes one approach to threading topics from AI, graphics, and parallel processing into coursework starting early in a student's program without substantially altering the basic course objectives. This, in turn, allows the student to sample topics early enough to make informed decisions when selecting electives.

Part 1 Computers in control systems: historical development digital computer interfacing structure of a digital control loop interface units data representation and aliasing. Part 2 The z-transformation: definition of the z-transform z-transform properties inverse z-transform pulse of z-transfer functions of systems discrete approximations numerical integration pole-zero mapping modified z-transform multi-rate sampling systems analysis of multi-rate systems fast-slow multi-rate systems closed-loop multi-rate systems. Part 3 Analysis of sampled-data control systems z-transformation of systems stability in discrete systems time domain analysis root locus analysis frequency domain analysis. Part 4 Digital compensator design: continuous domain design digital design digital root locas design state feedback design digital PID control disign deadbeat response design optimal control design controller design in the presence of noise. Part 5 Real-time computer control systems: hardware requirements the digital computer and peripheral equipment the real-time clock interface units software aspects controller implementation programming languages system design co-ordination sampling rate selection and quantization errors computer/device communications interrupt handling multi-tasking and background/foreground. Part 6 Advances in computer control: artificial intelligence expert systems examples of expert systems knowledge based systems in control engineering conventional controller design methods manual control methods fuzzy sets and rule-based control basic fuzzy operations fuzzy relations fuzzy control algorithms parallel processing techniques parallel computers algorithms for parallel (MIMD) computers programming parallel computers performance of parallel computers parallel processing in control engineering matrix algebra numerical integration fault tolerance. Appendices: Table of laplace and z-transforms continuous second-order systems response specifications frequency response characteristics the transputer and occam occam overview transputer systems and occam configuration vibration control of a flexible cantilever system simulation modelling and state estimation controller design user interface solutions.

We present an optical method for information watermarking of three-dimensional (3D) objects by digital holography. A hidden image is embedded by double phase encoding in a phase-shift digital hologram of the 3D object. We decode the watermarked hologram to reconstruct the hidden image and the 3D object. We use either the entire hologram or a part of it to decode the hidden image. Experiments are presented to illustrate the ability to recover both the 3D object and the decoded hidden image. Digital holograms of the 3D object are obtained by optical experiments. The watermarking process, 3D object reconstruction, and hidden image recovery are performed digitally. To the best of our knowledge, this is the first report of 3D object watermarking by use of a phase encoding technique and digital holography.

A major challenge in computational neuroscience is to achieve high performance for real-time simulations of full size brain networks. Recent advances in GPU technology provide massively parallel, low-cost and efficient hardware that is widely available on the computer market. However, the comparatively low-level programming that is necessary to create an efficient GPU-compatible implementation of neuronal network simulations can be challenging, even for otherwise experienced programmers. To resolve this problem a number of tools for simulating spiking neural networks (SNN) on GPUs have been developed [1,2], but using a particular simulator usually comes with restrictions to particular supported neuron models, synapse models or connectivity schemes. Besides being inconvenient, this can unduly influence the path of scientific enquiry. Here we present GeNN (GPU enhance neuronal networks), which builds on NVIDIA's common unified device architecture (CUDA) to enable a more flexible framework. CUDA allows programmers to write C-like code and execute it on NVIDIA’s massively parallel GPUs. However, in order to achieve good performance, it is critical but not trivial to make the right choices on how to parallelize a computational problem, organize its data in memory and optimize the memory access patterns. GeNN is based on the idea that much of this optimization can be cast into heuristics that allow the GeNN meta-compiler to generate optimized GPU code from a basic description of the neuronal network model in a minimal domain specific language of C function calls. For further simplification, this description may also be obtained by translating variables, dynamical equations and parameters from an external simulator into GeNN input files. We are developing this approach for the Brian 2 [3] and SpineCreator/SpineML [4] systems. Using a code generation approach in GeNN has important advantages: 1. A large number of different neuron and synapse models can be provided without performance losses in the final simulation code. 2. The generated simulator code can be optimized for the available GPU hardware and for the specific model. 3. The framework is intrinsically extensible: New GPU optimization strategies, including strategies of other simulators, can be added in the generated code for situations where they are effective. The first release version of GeNN is available at http://sourceforge.net/projects/genn. It has been built and optimized for simulating neuronal networks with an anatomical structure (separate neuron populations with sparse or dense connection patterns with the possibility to use some common learning rules). We have executed performance and scalability tests on an NVIDIA Tesla C2070 GPU with an Intel Xeon(R) E5-2609 CPU running Ubuntu 12.04 LTS. Our results show that as the network size increases, GPU simulations never fail to outperform CPU simulations. But we are also able to demonstrate the performance limits of using GPUs with GeNN under different scenarios of network connectivity, learning rules and simulation parameters, confirming the that GPU acceleration can differ largely depending on the particular details of the model of interest.

We propose a novel, to the best of our knowledge, and fast adaptive layer-based (ALB) method for generating a computer-generated hologram (CGH) with accurate depth information. A complex three-dimensional (3D) object is adaptively divided into layers along the depth direction according to its own non-uniformly distributed depth coordinates, which reduces the depth error caused by the conventional layer-based method. Each adaptive layer generates a single-layer hologram using the angular spectrum method for diffraction, and the final hologram of a complex three-dimensional object is obtained by superimposing all the adaptive layer holograms. A hologram derived with the proposed method is referred to as an adaptive layer-based hologram (ALBH). Our demonstration shows that the desired reconstruction can be achieved with 52 adaptive layers in 8.7 s, whereas the conventional method requires 397 layers in 74.9 s.

Research on convolutional neural network to realize high-quality dynamic holographic display

Holographic imaging offers a reliable and fast method to capture the complete three-dimensional (3D) information of the scene from a single perspective. We review our recently proposed single-channel optical system for generating digital Fresnel holograms of 3D real-existing objects illuminated by incoherent light. In this motionless holographic technique, light is reflected, or emitted from a 3D object, propagates through a spatial light modulator (SLM), and is recorded by a digital camera. The SLM is used as a beamsplitter of the single-channel incoherent interferometer, such that each spherical beam originated from each object point is split into two spherical beams with two different curve radii. Incoherent sum of the entire interferences between all the couples of spherical beams creates the Fresnel hologram of the observed 3D object. When this hologram is reconstructed in the computer, the 3D properties of the object are revealed.

Offset computation for expanding a polyhedral object by an offset radius is a fundamental geometric process frequently used in manufacturing applications. This process combined with the triple-dexel representation solid model has become popular because of its robustness and compatibility with parallel processing using a graphics processing unit (GPU). In parallel geometric processing, conflicts between threads must be avoided. Thus, we propose a novel parallel offsetting algorithm free from conflicts between threads. The triple-dexel model is a combination of x-, y-, and z-axis-aligned dexel models. Each dexel model is defined based on an orthogonal grid given on a coordinate plane. We subdivide the grid into several sub-grids of a fixed size in advance. For each sub-grid, a block of GPU threads is assigned. As each GPU thread always processes different dexel elements from the other threads in this method, no conflict occurs. Our research group has previously presented a parallel offset computation algorithm for a polyhedral solid model that also uses a triple-dexel representation model and a GPU. In the previous algorithm, the surface polygons of the model are classified into several groups in advance. The parallel offset computation of multiple polygon groups is realized by selecting groups of polygon in which the offset processing of the polygons does not affect one another. This selection process is time-consuming. Computational experiments were performed to analyze the performance difference between the current algorithm and our previous algorithm. In our experiments, the current algorithm achieved speedups of 1.4 times to 3.2 times compared to our previous offsetting algorithm.

This study presents a parallel acceleration framework for computational fluid dynamics (CFD) simulations in OpenFOAM through the integration of domestic general-purpose GPU (GPGPU) accelerator cards with two novel matrix coloring algorithms based on the LDU matrix format. Focusing on preconditioning techniques optimization, we specifically address the parallel implementation challenges of Diagonal Incomplete LU (DILU) decomposition and Gauss-Seidel smoother algorithms. To resolve critical data race issues inherent in parallel computations, we develop two innovative coloring strategies: (1) a hierarchical parallelization method utilizing the matrix's directed acyclic graph (DAG) topology for dependency sorting, and (2) an enhanced multicolor ordering algorithm derived from red-black Gauss-Seidel principles. Experimental validation using both motorBike benchmark and 3D lid-driven cavity cases demonstrates the proposed methods' computational efficacy. Comparative analysis reveals that our GPU-accelerated implementation achieves 5× speedup for standalone DILU/DIC preconditioners and 4-10× acceleration for hybrid DILU-Gauss-Seidel configurations compared to conventional CPU-based approaches. The developed framework maintains numerical accuracy while significantly enhancing solution efficiency, providing a robust acceleration paradigm for large-scale CFD simulations in heterogeneous computing environments.

Parallel Computing - Fundamentals, Applications and New Directions

GPU accelerators have become an Important backbone for scientific high performance-computing, and the performance advances obtained from adopting new GPU hardware are significant. In this paper we take a first look at NVIDIA's newest server-line GPU, the A100 architecture, part of the Ampere generation. Specifically, we assess its performance for sparse and batch computations, as these routines are relied upon in many scientific applications, and compare to the performance achieved on NVIDIA's previous server-line GPU.

Distributed parallel computing, which uses general-purpose workstations connected by a network as a large parallel computing resource, is one of the most promising trends in parallel computing. Over the last two decades, the growth in the use of such parallel computing environment has ensured the interest on the parallel finite element method. In this work, parallel finite element method using domain decomposition technique has been adapted to the distributed parallel environment of networked workstations and the supercomputer. Using the developed code, several model problems are solved and the parallel performances are analyzed on the network of eight Pentium IV PC cluster and GP7000F workstations. Finally, very large size of problem over 11 million DOFs has been solved using 1024 SR8000 supercomputer.

A parallel predictor-corrector (PC) iteration scheme for a general class of pseudo two-step Runge-Kutta methods (PTRK methods) of arbitrarily high order is analyzed for solving first-order nonstiff initial-value problems (IVPs) on parallel computers. Starting with ans-stage pseudo two-step RK method of orderp* withw implicit stages, we apply the highly parallel PC iteration process in P(EC)mE mode. The resulting parallel-iterated pseudo two-step RK method (PIPTRK method) uses an optimal number of processors equal tow. By a number of numerical experiments, we show the superiority of the PIPTRK methods proposed in this paper over both sequential and parallel methods available in the literature.

Virtual earthquake engineering laboratory with physics-based degrading materials on parallel computers