Browsing by Author "Ponce, Sean"

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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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    BABES: Brushing+Linking, Attributes, and Blobs Extension to Storyboard
    Judge, Tejinder K.; Kopper, Regis; Ponce, Sean; Silva, Mara G.; North, Christopher L. (Department of Computer Science, Virginia Polytechnic Institute & State University, 2008)
    In this day and age, people not only deal with data but deal with vast amounts of data which needs to be sorted and made sense of. A subset of these people are intelligence analysts who sort through an enormous amount of data that need to be organized to uncover plots and subplots. We are proposing a tool called BABES (Brushing+Linking, Attributes, and Blobs Extension to Storyboard) that will enable the intelligence analyst to sort through data efficiently, uncover plots and subplots using the brushing and linking and attributes features and work with multiple subplots at the same time using the concept of ’blobs’.