9021705
Integrated volume compression and visualization
Volumetric data sets require enormous storage capacity even at moderate resolution levels. The excessive storage demands not only stress the capacity of the underlying storage and communications systems, but also seriously limit the speed of volume rendering due to data movement and manipulation. A novel volumetric data visualization scheme is proposed and implemented in this work that renders 2D images directly from compressed 3D data sets. The novelty of this algorithm is that rendering is performed on the compressed representation of the volumetric data without pre-decompression. As a result, the overheads associated with both data movement and rendering processing are significantly reduced. The proposed algorithm generalizes previously proposed whole-volume frequency-domain rendering schemes by first dividing the 3D data set into subcubes, transforming each subcube to a frequency-domain representation, and applying the Fourier projection theorem to produce the projected 2D images according to given viewing angles. Compared to the whole-volume approach, the subcube-based scheme not only achieves higher compression efficiency by exploiting local coherency, but also improves the quality of resultant rendering images because it approximates the occlusion effect on a subcube by subcube basis.
6437408
Application-specific file prefetching for multimedia programs
This paper describes the design, implementation, and evaluation of an automatic application-specific file prefetching mechanism that is designed to improve the I/O performance of multimedia programs with complicated access patterns. The key idea of the proposed approach is to convert an application into two threads: a computation thread, which is the original program containing both computation and disk I/O, and a prefetch thread, which contains all the instructions in the original program that are related to disk accesses. At run time, the prefetch thread is scheduled to run far ahead of the computation thread, so that disk blocks can be prefetched and put in the file system buffer cache before the computation thread needs them. A source-to-source translator is developed to automatically generate the prefetch and computation thread from a given application program without any user intervention. We have successfully implemented a prototype of this automatic application-specific file prefetching mechanism under Linux. The prototype is shown to provide as much as 54% overall performance improvement for real-world multimedia applications.
35542914
A Decoupled Architecture for Application-Specific File Prefetching
5738405
Zodiac: A history-based interactive video authoring system
Abstract. Easy-to-use audio/video authoring tools play a crucial role in moving multimedia software from research curiosity to mainstream applications. However, research in multimedia authoring systems has rarely been documented in the literature. This paper describes the design and implementation of an interactive video authoring system called Zodiac, which employs an innovative edit history abstraction to support several unique editing features not found in existing commercial and research video editing systems. Zodiac provides users a conceptually clean and semantically powerful branching history model of edit operations to organize the authoring process, and to navigate among versions of authored documents. In addition, by analyzing the edit history, Zodiac is able to reliably detect a composed video stream's shot and scene boundaries, which facilitates interactive video browsing. Zodiac also features a video object annotation capability that allows users to associate annotations to moving objects in a video sequence. The annotations themselves could be text, image, audio, or video. Zodiac is built on top of MMFS, a file system specifically designed for interactive multimedia development environments, and implements an internal buffer manager that supports transparent lossless compression/decompression. Shot/scene detection, video object annotation, and buffer management all exploit the edit history information for performance optimization.
6822991
An Integrated Pipeline of Decompression, Simplification and Rendering for Irregular Volume Data
Very large irregular-grid volume data sets are typically represented as tetrahedral mesh and require substantial disk I/O and rendering computation. One effective way to reduce this demanding resource requirement is compression. Previous research showed how rendering and decompression of a losslessly compressed irregular-grid data set can be integrated into a one-pass computation. This work advances the state of the art one step further by showing that a losslessly compressed irregular volume data set can be simplified while it is being decompressed and that simplification, decompression, and rendering can again be integrated into a pipeline that requires only a single pass through the data sets. Since simplification is a form of lossy compression, the on-the-fly volume simplification algorithm provides a powerful mechanism to dynamically create versions of a tetrahedral mesh at multiple resolution levels directly from its losslessly compressed representation, which also corresponds to the finest resolution level. In particular, an irregular-grid volume renderer can exploit this multi-resolution representation to maintain interactivity on a given hardware/software platform by automatically adjusting the amount of rendering computation that could be afforded, or performing so called time-critical rendering. The proposed tetrahedral mesh simplification algorithm and its integration with volume decompression and rendering has been successfully implemented in the Gatun system. Performance measurements on the Gatun prototype show that simplification only adds less than 5% of performance overhead on an average and with multi-resolution pre-simplification the end-to-end rendering delay indeed decreases in an approximately linear fashion with respect to the simplification ratio.
5388492
On-the-Fly rendering of losslessly compressed irregular volume data
Very large irregular-grid data sets are represented as tetrahedral mesh and may incur significant disk I/O access overhead in the rendering process. An effective way to alleviate the disk I/O overhead associated with rendering large tetrahedral mesh is to reduce the I/O bandwidth requirement through compression. Existing tetrahedral mesh compression algorithms focus only on compression efficiency and cannot be readily integrated into the mesh rendering process, and thus demand that a compressed tetrahedral mesh be decompressed before it can be rendered into a 2D image. This paper presents an integrated tetrahedral mesh compression and rendering algorithm calledGatun, which allows compressed tetrahedral meshes to be rendered incrementally as they are being decompressed, thus forming an efficient irregular grid rendering pipeline. Both compression and rendering algorithms in Gatun exploit the same local connectivity information among adjacent tetrahedra, and thus can be tightly integrated into a unified implementation framework. Our tetrahedral compression algorithm is specifically designed to facilitate the integration with irregular grid renderer without any compromise in compression efficiency. A unique performance advantage of Gatunis its ability to reduce the run-time memory footprint requirement by releasing memory allocated to tetrahedra as early as possible. As a result, Gatunis able to decrease rendering time by a factor of 2 for very large tetrahedral mesh whose size exceeds the amount of physical memory. At the same time, the smaller working set and better access locality of Gatunimprove the rendering performance by up to 30%, even when the input tetrahedral mesh is entirely memory-resident. CR Categories: I.3.3 [Computer Graphics]: Picture/Image Generation—Display Algorithms; E.4 [Coding and Information Theory]: Data Compaction and Compression.
14699908
Performance Analysis of Applying Replica Selection Technology for Data Grid Environments
The Data Grid enables the sharing, selection, and connection of a wide variety of geographically distributed computational and storage resources for solving large-scale data intensive scientific applications. Such technology efficiently manage and transfer terabytes or even petabytes of data for data-intensive, high-performance computing applications in wide-area, distributed computing environments. Replica selection process allows an application to choose a replica from replica catalog, based on its performance and data access features. In this paper, we build a Grid environment based on three existing PC Cluster environments and perform performance analysis of data transfers using GridFTP protocol over these systems. In addition, based on experimental results, it is proposed a cost model to pick the best replica, in real and dynamic network situations.
10638554
Near-optimal regularization parameters for applications in computer vision
Computer vision requires the solution of many ill-posed problems such as optical flow, structure from motion, shape from shading, surface reconstruction, image restoration and edge detection. Regularization is a popular method to solve ill-posed problems, in which the solution is sought by minimization of a sum of two weighted terms, one measuring the error arising from the ill-posed model, the other indicating the distance between the solution and some class of solutions chosen on the basis of prior knowledge (smoothness, or other prior information). One of important issues in regularization is choosing optimal weight (or regularization parameter). Existing methods for choosing regularization parameters either require the prior information on noise in the data, or are heuristic graphical methods. We apply a method for choosing near-optimal regularization parameters by approximately minimizing the distance between the true solution and the family of regularized solutions. We demonstrate the effectiveness of this approach for the regularization on two examples: edge detection and image restoration.
39460490
Localization Techniques for Cluster-Based Data Grid
In this paper, we present an efficient method for optimizing localities of data distribution when executing data parallel applications. The data to logical grid nodes mapping technique is employed to enhance the performance of parallel programs on cluster grid. Cluster grid is a typical computational grid environment consists of several clusters located in multiple campuses that are distributed globally over the Internet. Objective of the proposed technique is to reduce inter-cluster communication overheads and to speed the execution of data parallel programs in the underlying distributed cluster grid. The theoretical analysis and experimental results show improvement of communication costs and scalable of the proposed techniques on different hierarchical cluster grids.
42663338
Automatic curve fitting with quadratic B-spline functions and its applications to computer-assisted animation
Abstract Automatic fitting to digitized curves by quadratic B-spline functions is discussed. Due to its simplicity, it is possible to find a quick fit within a given error tolerance bound which is defined as the maximum distance between the fitted curve and the original curve. Again due to the simple form of a quadratic polynomial, some complicated computations in computer-assisted animation such as hidden line removal can also be easily handled.
15261618
A Jobs Allocation Strategy for Multiple DRBL Diskless Linux Clusters with Condor Schedulers
In this paper, we construct four DRBL-based diskless Linux PC clusters and install Globus Toolkit and Condor scheduler on the master node of each cluster. All diskless clusters can be merged into a powerful computing environment. To utilize the computing power, we propose a jobs allocation strategy for application execution. We use HPL to conduct some performance evaluations and analyze those data to form a formula for jobs allocation. The information server will calculate the value of computing power based on our formula and find the most appropriate cluster to execute the job. It can make our large computing environment that can execute the jobs in a more efficient way