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Cluster-based tiled display walls can provide cost-effective and scalable displays with high resolution and a large display area. The software to drive them needs to scale too if arbitrarily large displays are to be built. Chromium is a popular software API used to construct such displays. Chromium transparently renders any OpenGL application to a tiled display by partitioning and sending individual OpenGL primitives to each client per frame. Visualization applications often deal with massive geometric data with millions of primitives. Transmitting them every frame results in huge network requirements that adversely affect the scalability of the system. In this paper, we present Garuda, a client-server-based display wall framework that uses off-the-shelf hardware and a standard network. Garuda is scalable to large tile configurations and massive environments. It can transparently render any application built using the Open Scene Graph (OSG) API to a tiled display without any modification by the user. The Garuda server uses an object-based scene structure represented using a scene graph. The server determines the objects visible to each display tile using a novel adaptive algorithm that culls the scene graph to a hierarchy of frustums. Required parts of the scene graph are transmitted to the clients, which cache them to exploit the interframe redundancy. A multicast-based protocol is used to transmit the geometry to exploit the spatial redundancy present in tiled display systems. A geometry push philosophy from the server helps keep the clients in sync with one another. Neither the server nor a client needs to render the entire scene, making the system suitable for interactive rendering of massive models. Transparent rendering is achieved by intercepting the cull, draw, and swap functions of OSG and replacing them with our own. We demonstrate the performance and scalability of the Garuda system for different configurations of display wall. We also show that the server and network loads grow sublinearly with the increase in the number of tiles, which makes our scheme suitable to construct very large displays.

In this article, we present a parallel prioritized Jacobian-based inverse kinematics algorithm for multithreaded architectures. We solve damped least squares inverse kinematics using a parallel line search by identifying and sampling critical input parameters. Parallel competing execution paths are spawned for each parameter in order to select the optimum that minimizes the error criteria. Our algorithm is highly scalable and can handle complex articulated bodies at interactive frame rates. We show results on complex skeletons consisting of more than 600 degrees of freedom while being controlled using multiple end effectors. We implement the algorithm both on multicore and GPU architectures and demonstrate how the GPU can further exploit fine-grain parallelism not directly available on a multicore processor. Our implementations are 10 to 150 times faster compared to a state-of-the-art serial implementation while providing higher accuracy. We also demonstrate the scalability of the algorithm over multiple scenarios and explore the GPU implementation in detail.

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