Kaloian Petkov

Efficient LBM Visual Simulation on Face-Centered Cubic Lattices

The Lattice Boltzmann method (LBM) for visual simulation of fluid flow generally employs cubic Cartesian (CC) lattices such as the D3Q13 and D3Q19 lattices for the particle transport. However, the CC lattices lead to suboptimal representation of the simulation space. We introduce the face-centered cubic (FCC) lattice, fD3Q13, for LBM simulations. Compared to the CC lattices, the fD3Q13 lattice creates a more isotropic sampling of the simulation domain and its single lattice speed (i.e., link length) simplifies the computations and data storage. Furthermore, the fD3Q13 lattice can be decomposed into two independent interleaved lattices, one of which can be discarded, which doubles the simulation speed. The resulting LBM simulation can be efficiently mapped to the GPU, further increasing the computational performance. We show the numerical advantages of the FCC lattice on channeled flow in 2D and the flow-past-a-sphere benchmark in 3D. In both cases, the comparison is against the corresponding CC lattices using the analytical solutions for the systems as well as velocity field visualizations. We also demonstrate the performance advantages of the fD3Q13 lattice for interactive simulation and rendering of hot smoke in an urban environment using thermal LBM.

Implementing the lattice Boltzmann model on commodity graphics hardware

Modern graphics processing units (GPUs) can perform general-purpose computations in addition to the native specialized graphics operations. Due to the highly parallel nature of graphics processing, the GPU has evolved into a many-core coprocessor that supports high data parallelism. Its performance has been growing at a rate of squared Moores law, and its peak floating point performance exceeds that of the CPU by an order of magnitude. Therefore, it is a viable platform for time-sensitive and computationally intensive applications. The lattice Boltzmann model (LBM) computations are carried out via linear operations at discrete lattice sites, which can be implemented efficiently using a GPU-based architecture. Our simulations produce results comparable to the CPU version while improving performance by an order of magnitude. We have demonstrated that the GPU is well suited for interactive simulations in many applications, including simulating fire, smoke, lightweight objects in wind, jellyfish swimming in water, and heat shimmering and mirage (using the hybrid thermal LBM). We further advocate the use of a GPU cluster for large scale LBM simulations and for high performance computing. The Stony Brook Visual Computing Cluster has been the platform for several applications, including simulations of real-time plume dispersion in complex urban environments and thermal fluid dynamics in a pressurized water reactor. Major GPU vendors have been targeting the high performance computing market with GPU hardware implementations. Software toolkits such as NVIDIA CUDA provide a convenient development platform that abstracts the GPU and allows access to its underlying stream computing architecture. However, software programming for a GPU cluster remains a challenging task. We have therefore developed the Zippy framework to simplify GPU cluster programming. Zippy is based on global arrays combined with the stream programming model and it hides the low-level details of the underlying cluster architecture.

The Reality Deck--an Immersive Gigapixel Display

The Reality Deck is a visualization facility offering state-of-the-art aggregate resolution and immersion. Its a 1.5-Gpixel immersive tiled display with a full 360-degree horizontal field of view. Comprising 416 high-density LED-backlit LCD displays, it visualizes gigapixel-resolution data while providing 20/20 visual acuity for most of the visualization space.

Remote volume rendering pipeline for mHealth applications

We introduce a novel remote volume rendering pipeline for medical visualization targeted for mHealth (mobile health) applications. The necessity of such a pipeline stems from the large size of the medical imaging data produced by current CT and MRI scanners with respect to the complexity of the volumetric rendering algorithms. For example, the resolution of typical CT Angiography (CTA) data easily reaches 512^3 voxels and can exceed 6 gigabytes in size by spanning over the time domain while capturing a beating heart. This explosion in data size makes data transfers to mobile devices challenging, and even when the transfer problem is resolved the rendering performance of the device still remains a bottleneck. To deal with this issue, we propose a thin-client architecture, where the entirety of the data resides on a remote server where the image is rendered and then streamed to the client mobile device. We utilize the display and interaction capabilities of the mobile device, while performing interactive volume rendering on a server capable of handling large datasets. Specifically, upon user interaction the volume is rendered on the server and encoded into an H.264 video stream. H.264 is ubiquitously hardware accelerated, resulting in faster compression and lower power requirements. The choice of low-latency CPU- and GPU-based encoders is particularly important in enabling the interactive nature of our system. We demonstrate a prototype of our framework using various medical datasets on commodity tablet devices.

Frameless Volume Visualization

We have developed a novel visualization system based on the reconstruction of high resolution and high frame rate images from a multi-tiered stream of samples that are rendered framelessly. This decoupling of the rendering system from the display system is particularly suitable when dealing with very high resolution displays or expensive rendering algorithms, where the latency of generating complete frames may be prohibitively high for interactive applications. In contrast to the traditional frameless rendering technique, we generate the lowest latency samples on the optimal sampling lattice in the 3D domain. This approach avoids many of the artifacts associated with existing sample caching and reprojection methods during interaction that may not be acceptable in many visualization applications. Advanced visualization effects are generated remotely and streamed into the reconstruction system using tiered samples with varying latencies and quality levels. We demonstrate the use of our visualization system for the exploration of volumetric data at stable guaranteed frame rates on high resolution displays, including a 470 megapixel tiled display as part of the Reality Deck immersive visualization facility.

Enclosed Five-Wall Immersive Cabin

We present a novel custom-built 3D immersive environment, called the Immersive Cabin (IC). The IC is fully enclosed with an automatic door on the rear screen, and thus very different from existing CAVE environments. Our IC, the construction of the projection screens and stereo projectors as well as the calibration procedure are explained in details. The projectors are driven by our Visual Computing cluster for computation and rendering. Three applications that have been developed on the IC are described, 3D virtual colonoscopy, dispersion simulation for urban security, and 3D imagery and artistic creations.

Visual exploration of the infinite canvas

We introduce the concept of the infinite canvas as a metaphor for the immersive visual exploration of very large image datasets using a natural walking interface. The interface allows the user to move along the display surface and to be continuously exposed to new data, essentially exploring the horizontal axis of an arbitrarily long canvas. Our system provides a spiral navigation interface that shows a compressed immersive overview of the data and facilitates the rapid and fluid transition to distant points within the infinite canvas. We demonstrate the implementation of the infinite canvas in the worlds first 1.5 billion pixel tiled immersive display.

Method of Moments software for GPU hardware

The Method of Moments (MoM) technique is the backbone of all computational methods for the modeling and simulation of complex systems. With applications including fluid mechanics, electromagnetics, and fracture modeling, MoM is versatile and has laid the foundation for modern optimization methods. Modeling and simulation is absolutely necessary for the success of all complex engineering problems of today. Unfortunately, the size and complexity of some problems cause computations to be extremely time consuming. Even with optimized MoM methods, such as the Fast Multipole Method (FMM), some simulations can take days or weeks to complete with acceptable accuracy. Due to the limitations of traditional CPU hardware, research has been expanding to develop computation methods for Graphics Processing Unit (GPU) hardware. The GPU, which refers to the commodity off-the-shelf 3D graphics card, is specifically designed to be extremely fast at processing large graphics data sets (e.g., polygons and pixels). The computational power of todays commodity GPUs has exceeded that of PC-based CPUs. As the semiconductor fabrication technology advances, GPUs can use this additional hardware capability much more efficiently for computation than CPUs by increasing the number of computational “pipelines” (database, software networking modules and computational power). Additionally, many of the complex applications for MoM have computational patterns which are easily parallelizable and hence can be accelerated on commodity GPUs, achieving near real-time computation on ordinary PCs and laptops. This means that computational intensive modeling and simulation using GPUs is now becoming a realistic design tool. This paper presents the process and results of creating Method of Moments software that utilizes the parallelization benefits of GPU hardware. Written in a GPU language, CUDA, this software shows great potential for the future of complex modeling and simulation.