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.
Compared to the system level solution, a lot more parallelization efforts for visualization focus on parallel rendering, ranging from photo-realistic rendering @cite_15 , volume rendering @cite_20 to parallel iso-surfacing @cite_21 . Among a large set of previous work specific to parallel polygon rendering, Crockett @cite_7 harnessed message passing architectures for polygon rendering parallelism that reduces memory usage and network contention while overlapping computation and communication. He also gives an overview of parallel rendering techniques from both hardware and software prospectives later on @cite_12 .
@cite_7 @cite_21 @cite_15 @cite_20 @cite_12
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Applications such as real-time animation and scientific visualization demand high performance for rendering complex 3D abstract data models into 2D images. As large applications migrate to highly parallel supercomputers, how can we exploit the available parallelism to keep the rendering on the supercomputer? To answer this question, we developed a parallel polygon renderer for general-purpose MIMD distributed-memory message-passing systems. It exploits object-level and image-level parallelism, and can run on systems containing from one processor to a number bounded by the number of scan lines in the resulting image. Unlike earlier approaches, ours multiplexes the transformation and rasterization phases on the same machine. This reduces memory usage and network contention, and overlaps computation and communication. > Parallel rendering of large polygonal models with transparency is challenging due to the need for alpha-correct blending and compositing, which is costly for very large models with high depth complexity and spatial overlap. In this paper we compare the performance of raster-based rendering methods on mesh models of neurons using two applications, one of which is specifically tailored to the neuroscience application domain, the other a general purpose visualization tool with domain specific additions. The first implements both sort-first and sort-last and uses a scene graph style traversal to cull objects, and dual depth peeling for order independent transparency, whilst the other uses a simpler brute force data-parallel approach with sort last composition. The advantages and trade offs of these approaches are discussed. We present the optimized algorithms needed to achieve interactive frame rates for a non-trivial, real-world parallel rendering scenario. We show that a generic data visualization application can provide competitive performance when optimizing its rendering pipeline, with some loss of capability over an optimized domain-specific application. With faster graphics hardware comes the possibility to realize even more complicated applications that require more detailed data and provide better presentation. The processors keep being challenged with bigger amount of data and higher resolution outputs, requiring more research in the parallel distributed rendering domain. Optimizing resource usage to improve throughput is one important topic, which we address in this article for multi-display applications, using the Equalizer parallel rendering framework. This paper introduces and analyzes cross-segment load balancing which efficiently assigns all available shared graphics resources to all display output segments with dynamical task partitioning to improve performance in parallel rendering Using parallel processing for visualization speeds up computer graphics rendering of complex data sets. A parallel algorithm designed for polygon scan conversion and rendering is presented which supports fast rendering of highly complex data sets using advanced lighting models. Dedicated graphics rendering engines do not necessarily suit such data sets, although they can support real-time update of moderately complex scenes using simple lighting. Advantages to using a software-based approach include the feasibility of adding special rendering features to the program and the capability of integrating a parallel scientific application with a parallel graphics renderer. A new work decomposition strategy presented, called task adaptive, is based on dynamically partitioning the amount of computational work left at a given time. The algorithm uses a heuristic for dynamic task decomposition in which image space tasks are partitioned without requiring interruption of the partitioned processor. A sophisticated memory referencing strategy lets local memory access graphics data during rendering. This permits implementation of the algorithm on a distributed memory multiprocessor. An in-depth analysis of the overhead costs accompanying parallel processing shows where performance is adequate or could be improved. >