Browsing by Author "Park, Seung In"

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    An Application-Oriented Approach for Accelerating Data-Parallel Computation with Graphics Processing Unit
    Ponce, Sean; Jing, Huang; Park, Seung In; Khoury, Chase; Quek, Francis; Cao, Yong (Department of Computer Science, Virginia Polytechnic Institute & State University, 2009-03-01)
    This paper presents a novel parallelization and quantitative characterization of various optimization strategies for data-parallel computation on a graphics processing unit (GPU) using NVIDIA's new GPU programming framework, Compute Unified Device Architecture (CUDA). CUDA is an easy-to-use development framework that has drawn the attention of many different application areas looking for dramatic speed-ups in their code. However, the performance tradeoffs in CUDA are not yet fully understood, especially for data-parallel applications. Consequently, we study two fundamental mathematical operations that are common in many data-parallel applications: convolution and accumulation. Specifically, we profile and optimize the performance of these operations on a 128-core NVIDIA GPU. We then characterize the impact of these operations on a video-based motion-tracking algorithm called vector coherence mapping, which consists of a series of convolutions and dynamically weighted accumulations, and present a comparison of different implementations and their respective performance profiles.
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    Modeling Social Group Interactions for Realistic Crowd Behaviors
    Park, Seung In (Virginia Tech, 2013-03-22)
    In the simulation of human crowd behavior including evacuation planning, transportation management, and safety engineering in architecture design, the development of pedestrian model for higher behavior fidelity is an important task. To construct plausible facsimiles of real crowd movements, simulations should exhibit human behaviors for navigation, pedestrian decision-making, and social behaviors such as grouping and crowding. The research field is quite mature in some sense, with a large number of approaches that have been proposed to path finding, collision avoidance, and visually pleasing steering behaviors of virtual humans. However, there is still a clear disparity between the variety of approaches and the quality of crowd behaviors in simulations. Many social science field studies inform us that crowds are typically composed of multiple social groups (James, 1953; Coleman and James, 1961; Aveni, 1977). These observations indicate that one component of the complexity of crowd dynamics emerges from the presence of various patterns of social interactions within small groups that make up the crowd. Hence, realism in a crowd simulation may be enhanced when virtual characters are organized in multiple social groups, and exhibit human-like coordination behaviors. Motivated by the need for modeling groups in a crowd, we present a multi-agent model for large crowd simulations that incorporates socially plausible group behaviors. A computational model for multi-agent coordination and interaction informed by well- established Common Ground theory (Clark, 1996; Clark and Brennan, 1991) is proposed. In our approach, the task of navigation in a group is viewed as performing a joint activity which requires maintaining a state of common ground among group members regarding walking strategies and route choices. That is, group members communicate with, and adapt their behaviors to each other in order to maintain group cohesiveness while walking. In the course of interaction, an agent may present gestures or other behavioral cues according to its communicative purpose. It also considers the spatiotemporal conditions of the agent-group's environment in which the agent interacts when selecting a kind of motions. With the incorporation of our agent model, we provide a unified framework for crowd simulation and animation which accommodates high-level socially-aware behavioral realism of animated characters. The communicative purpose and motion selection of agents are consistently carried through from simulation to animation, and a resulted sequence of animated character behaviors forms not merely a chain of reactive or random gestures but a socially meaningful interactions. We conducted several experiments in order to investigate the impact of our social group interaction model in crowd simulation and animation. By showing that group communicative behaviors have a substantial influence on the overall distribution of a crowd, we demonstrate the importance of incorporating a model of social group interaction into multi-agent simulations of large crowd behaviors. With a series of perceptual user studies, we show that our model produces more believable behaviors of animated characters from the viewpoint of human observers.
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    Performance Analysis of a Novel GPU Computation-to-core Mapping Scheme for Robust Facet Image Modeling
    Park, Seung In; Cao, Yong; Watson, Layne T.; Quek, Francis (Department of Computer Science, Virginia Polytechnic Institute & State University, 2012)
    Though the GPGPU concept is well-known in image processing, much more work remains to be done to fully exploit GPUs as an alternative computation engine. This paper investigates the computation-to-core mapping strategies to probe the efficiency and scalability of the robust facet image modeling algorithm on GPUs. Our fine-grained computation-to-core mapping scheme shows a significant performance gain over the standard pixel-wise mapping scheme. With in-depth performance comparisons across the two different mapping schemes, we analyze the impact of the level of parallelism on the GPU computation and suggest two principles for optimizing future image processing applications on the GPU platform.