We present a parallel visualization algorithm for the illustrative rendering of depth-dependent stylized dense tube data at interactive frame rates. While this computation could be efficiently performed on a GPU device, we target a parallel framework to enable it to be efficiently running on an ordinary multi-core CPU platform which is much more available than GPUs for common users. Our approach is to map the depth information in each tube onto each of the visual dimensions of shape, color, texture, value, and size on the basis of Bertin's semiology theory. The purpose is to enable more legible displays in the dense tube environments. A major contribution of our work is an efficient and effective parallel depthordering algorithm that makes use of the message passing interface (MPI) with VTK. We evaluated our framework with visualizations of depth-stylized tubes derived from 3D diffusion tensor MRI data by comparing its efficiency with several other alternative parallelization platforms running the same computations. As our results show, the parallelization framework we proposed can efficiently render highly dense 3D data sets like the tube data and thus is useful as a complement to parallel visualization environments that rely on GPUs.
Note that the parallel sorting problem @cite_5 @cite_4 , which is at the core of our parallelization framework here, would be easily solved in highly efficient on GPU computing platforms with extensive existing algorithms available @cite_10 @cite_16 . In this paper instead, we target a cheaper solution without relying on high-end computing resources such as GPUs. Alternatively and complementarily, we use CPU-based parallel sorting algorithms leveraging a single processor of multiple cores, which has been almost a bottom-line configuration for modern common computers.
@cite_5 @cite_16 @cite_4 @cite_10
2752885492 1995596660 1591066263 1597508330
From the Publisher: The updated new edition of the classic Introduction to Algorithms is intended primarily for use in undergraduate or graduate courses in algorithms or data structures. Like the first edition,this text can also be used for self-study by technical professionals since it discusses engineering issues in algorithm design as well as the mathematical aspects. In its new edition,Introduction to Algorithms continues to provide a comprehensive introduction to the modern study of algorithms. The revision has been updated to reflect changes in the years since the book's original publication. New chapters on the role of algorithms in computing and on probabilistic analysis and randomized algorithms have been included. Sections throughout the book have been rewritten for increased clarity,and material has been added wherever a fuller explanation has seemed useful or new information warrants expanded coverage. As in the classic first edition,this new edition of Introduction to Algorithms presents a rich variety of algorithms and covers them in considerable depth while making their design and analysis accessible to all levels of readers. Further,the algorithms are presented in pseudocode to make the book easily accessible to students from all programming language backgrounds. Each chapter presents an algorithm,a design technique,an application area,or a related topic. The chapters are not dependent on one another,so the instructor can organize his or her use of the book in the way that best suits the course's needs. Additionally,the new edition offers a 25 increase over the first edition in the number of problems,giving the book 155 problems and over 900 exercises thatreinforcethe concepts the students are learning. This paper presents an algorithm for fast sorting of large lists using modern GPUs. The method achieves high speed by efficiently utilizing the parallelism of the GPU throughout the whole algorithm. Initially, GPU-based bucketsort or quicksort splits the list into enough sublists then to be sorted in parallel using merge-sort. The algorithm is of complexity nlogn, and for lists of 8 M elements and using a single Geforce 8800 GTS-512, it is 2.5 times as fast as the bitonic sort algorithms, with standard complexity of n(logn)^2, which for a long time was considered to be the fastest for GPU sorting. It is 6 times faster than single CPU quicksort, and 10 faster than the recent GPU-based radix sort. Finally, the algorithm is further parallelized to utilize two graphics cards, resulting in yet another 1.8 times speedup. Apparatus for supporting different nets for various sporting purposes including interengaging tubular rods which are arranged to interconnect and have ground engaging portions suitable to be useful for the several functions. The frame of the net support structure includes a pair of spaced apart, vertically extending posts; each of the posts is divided into a pair of telescoping sections. An upper horizontally extending multi-section member extends and connects the upper end of the vertical posts. A U-shaped clip is provided to engage the frame support with resilient holding pressure for supporting a net on the frame. “GPU Gems 2 isn't meant to simply adorn your bookshelf-it's required reading for anyone trying to keep pace with the rapid evolution of programmable graphics. If you're serious about graphics, this book will take you to the edge of what the GPU can do.” -Remi Arnaud, Graphics Architect at Sony Computer Entertainment “The topics covered in GPU Gems 2 are critical to the next generation of game engines.” -Gary McTaggart, Software Engineer at Valve, Creators of Half-Life and Counter-Strike This sequel to the best-selling, first volume of GPU Gems details the latest programming techniques for today's graphics processing units (GPUs). As GPUs find their way into mobile phones, handheld gaming devices, and consoles, GPU expertise is even more critical in today's competitive environment. Real-time graphics programmers will discover the latest algorithms for creating advanced visual effects, strategies for managing complex scenes, and advanced image processing techniques. Readers will also learn new methods for using the substantial processing power of the GPU in other computationally intensive applications, such as scientific computing and finance. Twenty of the book's forty-eight chapters are devoted to GPGPU programming, from basic concepts to advanced techniques. Written by experts in cutting-edge GPU programming, this book offers readers practical means to harness the enormous capabilities of GPUs.Major topics covered include: Geometric Complexity Shading, Lighting, and Shadows High-Quality Rendering General-Purpose Computation on GPUs: A Primer Image-Oriented Computing Simulation and Numerical Algorithms Contributors are from the following corporations and universities:1C: Maddox Games 2015 Apple Computer Armstrong State University Climax Entertainment Crytek discreet ETH Zurich GRAVIR IMAG-INRIA GSC Game World Lionhead Studios Lund University Massachusetts Institute of Technology mental images Microsoft Research NVIDIA Corporation Piranha Bytes Siemens Corporate Research Siemens Medical Solutions Simutronics Corporation Sony Pictures Imageworks Stanford University Stony Brook University Technische Universitat Munchen University of California, Davis University of North Carolina at Chapel Hill University of Potsdam University of Tokyo University of Toronto University of Utah University of Virginia University of Waterloo Vienna University of Technology VRVis Research CenterSection editors include NVIDIA engineers: Kevin Bjorke, Cem Cebenoyan, Simon Green, Mark Harris, Craig Kolb, and Matthias WlokaThe accompanying CD-ROM includes complementary examples and sample programs.