Volumetric real-time particle-based representation of large unstructured tetrahedral polygon meshes

In this paper we propose a particle-based volume rendering approach for unstructured, three-dimensional, tetrahedral polygon meshes. We stochastically generate millions of particles per second and project them on the screen in real-time. In contrast to previous rendering techniques of tetrahedral volume meshes, our method does not need a prior depth sorting of geometry. Instead, the rendered image is generated by choosing particles closest to the camera. Furthermore, we use spatial superimposing. Each pixel is constructed from multiple subpixels. This approach not only increases projection accuracy, but allows also a combination of subpixels into one superpixel that creates the well-known translucency effect of volume rendering. We show that our method is fast enough for the visualization of unstructured three-dimensional grids with hard real-time constraints and that it scales well for a high number of particles

Parallel cloth simulation using OpenMp and CUDA

The widespread availability of parallel computing architectures has lead to research regarding algorithms and techniques that best exploit available parallelism. In addition to the CPU parallelism available; the GPU has emerged as a parallel computational device. The goal of this study was to explore the combined use of CPU and GPU parallelism by developing a hybrid parallel CPU/GPU cloth simulation application. In order to evaluate the benefits of the hybrid approach, the application was first developed in sequential CPU form, followed by a parallel CPU form. The application uses Backward Euler implicit time integration to solve the differential equations of motion associated with the physical system. The Conjugate Gradient (CG) algorithm is used to determine the solution vector for the system of equations formed by the Backward Euler approach. The matrix/vector, vector/vector, and vector/scalar operations required by CG are handled by calls to BLAS level 1 and level 2 functions. In the sequential CPU and parallel CPU versions, the Intel Math Kernel Library implementation of BLAS is used. In the hybrid parallel CPU/GPU version, the Nvidia CUDA based BLAS implementation (CUBLAS) is used. In the parallel CPU and hybrid implementations, OpenMP directives are used to parallelize the force application loop that traverses the list of forces acting on the system. Runtimes were collected for each version of the application while simulating cloth meshes with particle resolutions of 20x20, 40x40, and 60x60. The performance of each version was compared at each mesh resolution. The level of performance degradation experienced when transitioning to the larger mesh sizes was also determined. The hybrid parallel CPU/GPU implementation yielded the highest frame rate for the 40x40 and 60x60 meshes. The parallel CPU implementation yielded the highest frame rate for the 20x20 mesh. The performance of the hybrid parallel CPU/GPU implementation degraded the least as it transitioned to the two larger mesh sizes. The results of this study will potentially lead to further research regarding the use of GPUs to perform the matrix/vector operations associated with the CG algorithm under more complex cloth simulation scenarios

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

Parallel, distributed and GPU computing technologies in single-particle electron microscopy

An introduction to the current paradigm shift towards concurrency in software