Jason Sewall

Fast animation of turbulence using energy transport and procedural synthesis

We present a novel technique for the animation of turbulent fluids by coupling a procedural turbulence model with a numerical fluid solver to introduce subgrid-scale flow detail. From the large-scale flow simulated by the solver, we model the production and behavior of turbulent energy using a physically motivated energy model. This energy distribution is used to synthesize an incompressible turbulent velocity field, whose features show plausible temporal behavior through a novel Lagrangian approach for advected noise. The synthesized turbulent flow has a dynamical effect on the large-scale flow, and produces visually plausible detailed features on both gaseous and free-surface liquid flows. Our method is an order of magnitude faster than full numerical simulation of equivalent resolution, and requires no manual direction.

Continuum traffic simulation

We present a novel method for the synthesis and animation of realistic traffic flows on large‐scale road networks. Our technique is based on a continuum model of traffic flow we extend to correctly handle lane changes and merges, as well as traffic behaviors due to changes in speed limit. We demonstrate how our method can be applied to the animation of many vehicles in a large‐scale traffic network at interactive rates and show that our method can simulate believable traffic flows on publicly‐available, real‐world road data. We furthermore demonstrate the scalability of this technique on many‐core systems.

Fast and Efficient Graph Traversal Algorithm for CPUs: Maximizing Single-Node Efficiency

Graph-based structures are being increasingly used to model data and relations among data in a number of fields. Graph-based databases are becoming more popular as a means to better represent such data. Graph traversal is a key component in graph algorithms such as reachability and graph matching. Since the scale of data stored and queried in these databases is increasing, it is important to obtain high performing implementations of graph traversal that can efficiently utilize the processing power of modern processors. In this work, we present a scalable Breadth-First Search Traversal algorithm for modern multi-socket, multi-core CPUs. Our algorithm uses lock- and atomic-free operations on a cache-resident structure for arbitrary sized graphs to filter out expensive main memory accesses, and completely and efficiently utilizes all available bandwidth resources. We propose a work distribution approach for multi-socket platforms that ensures load-balancing while keeping cross-socket communication low. We provide a detailed analytical model that accurately projects the performance of our single- and multi-socket traversal algorithms to within 5-10% of obtained performance. Our analytical model serves as a useful tool to analyze performance bottlenecks on modern CPUs. When measured on various synthetic and real-world graphs with a wide range of graph sizes, vertex degrees and graph diameters, our implementation on a dual-socket Intel® Xeon® X5570 (Intel microarchitecture code name Nehalem) system achieves 1.5X-13.2X performance speedup over the best reported numbers. We achieve around 1 Billion traversed edges per second on a scale-free R-MAT graph with 64M vertices and 2 Billion edges on a dual-socket Nehalem system. Our optimized algorithm is useful as a building block for efficient multi-node implementations and future exascale systems, thereby allowing them to ride the trend of increasing per-node compute and bandwidth resources.

Interactive hybrid simulation of large-scale traffic

We present a novel, real-time algorithm for modeling large-scale, realistic traffic using a hybrid model of both continuum and agent-based methods for traffic simulation. We simulate individual vehicles in regions of interest using state-of-the-art agent-based models of driver behavior, and use a faster continuum model of traffic flow in the remainder of the road network. Our key contributions are efficient techniques for the dynamic coupling of discrete vehicle simulation with the aggregated behavior of continuum techniques for traffic simulation. We demonstrate the flexibility and scalability of our interactive visual simulation technique on extensive road networks using both real-world traffic data and synthetic scenarios. These techniques demonstrate the applicability of hybrid techniques to the efficient simulation of large-scale flows with complex dynamics.

Transforming GIS Data into Functional Road Models for Large-Scale Traffic Simulation

There exists a vast amount of geographic information system (GIS) data that model road networks around the world as polylines with attributes. In this form, the data are insufficient for applications such as simulation and 3D visualization-tools which will grow in power and demand as sensor data become more pervasive and as governments try to optimize their existing physical infrastructure. In this paper, we propose an efficient method for enhancing a road map from a GIS database to create a geometrically and topologically consistent 3D model to be used in real-time traffic simulation, interactive visualization of virtual worlds, and autonomous vehicle navigation. The resulting representation provides important road features for traffic simulations, including ramps, highways, overpasses, legal merge zones, and intersections with arbitrary states, and it is independent of the simulation methodologies. We test the 3D models of road networks generated by our algorithm on real-time traffic simulation using both macroscopic and microscopic techniques.

Virtualized Traffic: Reconstructing Traffic Flows from Discrete Spatiotemporal Data

We present a novel concept, Virtualized Traffic, to reconstruct and visualize continuous traffic flows from discrete spatio-temporal data provided by traffic sensors or generated artificially to enhance a sense of immersion in a dynamic virtual world. Given the positions of each car at two recorded locations on a highway and the corresponding time instances, our approach can reconstruct the traffic flows (i.e. the dynamic motions of multiple cars over time) in between the two locations along the highway for immersive visualization of virtual cities or other environments. Our algorithm is applicable to high-density traffic on highways with an arbitrary number of lanes and takes into account the geometric, kinematic, and dynamic constraints on the cars. Our method reconstructs the car motion that automatically minimizes the number of lane changes, respects safety distance to other cars, and computes the acceleration necessary to obtain a smooth traffic flow subject to the given constraints. Furthermore, our framework can process a continuous stream of input data in real time, enabling the users to view virtualized traffic events in a virtual world as they occur.

Large-scale fluid simulation using velocity-vorticity domain decomposition

Simulating fluids in large-scale scenes with appreciable quality using state-of-the-art methods can lead to high memory and compute requirements. Since memory requirements are proportional to the product of domain dimensions, simulation performance is limited by memory access, as solvers for elliptic problems are not compute-bound on modern systems. This is a significant concern for large-scale scenes. To reduce the memory footprint and memory/compute ratio, vortex singularity bases can be used. Though they form a compact bases for incompressible vector fields, robust and efficient modeling of nonrigid obstacles and free-surfaces can be challenging with these methods. We propose a hybrid domain decomposition approach that couples Eulerian velocity-based simulations with vortex singularity simulations. Our formulation reduces memory footprint by using smaller Eulerian domains with compact vortex bases, thereby improving the memory/compute ratio, and simulation performance by more than 1000x for single phase flows as well as significant improvements for free-surface scenes. Coupling these two heterogeneous methods also affords flexibility in using the most appropriate method for modeling different scene features, as well as allowing robust interaction of vortex methods with free-surfaces and nonrigid obstacles.

Virtualized Traffic: Reconstructing Traffic Flows from Discrete Spatio-Temporal Data

We present a novel concept, Virtualized Traffic, to reconstruct and visualize continuous traffic flows from discrete spatio-temporal data provided by traffic sensors or generated artificially to enhance a sense of immersion in a dynamic virtual world. Given the positions of each car at two recorded locations on a highway and the corresponding time instances, our approach can reconstruct the traffic flows (i.e. the dynamic motions of multiple cars over time) in between the two locations along the highway for immersive visualization of virtual cities or other environments. Our algorithm is applicable to high-density traffic on highways with an arbitrary number of lanes and takes into account the geometric, kinematic, and dynamic constraints on the cars. Our method reconstructs the car motion that automatically minimizes the number of lane changes, respects safety distance to other cars, and computes the acceleration necessary to obtain a smooth traffic flow subject to the given constraints. Furthermore, our framework can process a continuous stream of input data in real time, enabling the users to view virtualized traffic events in a virtual world as they occur.

Visual simulation of shockwaves

We present an efficient method for visual simulations of shock phenomena in compressible, inviscid fluids. Our algorithm is derived from one class of the finite volume method especially designed for capturing shock propagation, but offers improved efficiency through physically-based simplification and adaptation for graphical rendering. Our technique is well suited for parallel implementation on multicore architectures and is also capable of handling complex, bidirectional object-shock interactions stably and robustly. We describe its applications to various visual effects, including explosion, sonic booms and turbulent flows.

Fast Simulation of Laplacian Growth

Laplacian instability is the physical mechanism driving pattern formation in many disparate natural phenomena. Current algorithms for simulating this instability are slow and memory intensive. A new algorithm, based on the dielectric breakdown model from physics, is more than three orders of magnitude faster than previous methods and decreases memory use by two orders of magnitude. Our algorithm admits a spherical harmonic solution, letting it account for arbitrary boundary data, such as an environment map