High Performance Computing (HPC) produces enormous amounts of data. This simple truth has been the perennial bane of the HPC user and there is no sign of the problem going away. The results of the computational proces...
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This paper introduces a novel technique to compute illumination for Direct Volume Rendering. By adding shadow effects to volume rendered images, the perception of shapes and tissue properties can be significantly impr...
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作者：
Akiba, HiroshiMa, Kwan-Liu
Department of Computer Science University of California at Davis United States
The dataset generated by a large-scale numerical simulation may include thousands of timesteps and hundreds of variables describing different aspects of the modeled physical phenomena. In order to analyze and understa...
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The large size and thin layers of radomes often precludes the use of full wave analysis tools. A method is presented here for plane wave multilayer dielectric radome analysis using a field matching model. parallel ray...
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We present transparent Direct3D9 application-level parallel rendering system named D3DPR. It allows Direct3D9 application to run on graphics cluster with no modification. graphics cluster can be classified as two type...
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ISBN:
(纸本)9781424415786
We present transparent Direct3D9 application-level parallel rendering system named D3DPR. It allows Direct3D9 application to run on graphics cluster with no modification. graphics cluster can be classified as two types of logical node, i.e. resource distributing node (D-Node) and resource rendering node (R-Node) by D3DPR. Among them, D-Node is responsible for converting Direct3D9 application to four kinds of rendering resource including command stream, vertex stream, index stream and texture stream. R-Node is responsible for reconstructing Direct3D9 interface rendering command based on the description information and resource data of received rendering resource. Each R-Node reserves entire rendering resource and distributes rendering task by computing the projective screen region of multi-stream based scene data bounding box. Experimental results have demonstrated that D3DPR parallel rendering system can not only achieve high-resolution tiled display but also promote rendering performance for Direct3D9 application running on graphics cluster.
Synthetic aperture radar data presents specific problems for interactive visualization. The high amount of multiplicative speckle noise has to be reduced. The high dynamic range of the amplitude data must be mapped to...
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Synthetic aperture radar data presents specific problems for interactive visualization. The high amount of multiplicative speckle noise has to be reduced. The high dynamic range of the amplitude data must be mapped to the lower dynamic range of display devices in a way that makes image features appropriately visible. In addition to interactive navigation in the data, it is desirable to allow interactive selection of despeckling and dynamic range reduction methods and adjustment of their parameters. graphics processing units (GPUs) can be seen as ubiquitous parallel coprocessors with extreme computational power. In this paper, we propose a GPU-based framework for interactive visualization of SAR data. data management techniques are used to make full use of the GPU. We reworked well-known despeckling and dynamic range reduction techniques for the GPU programming model and implemented them in our framework. Both navigation in largedata sets and adjustment of processing parameters are fully interactive.
The next decades will see an explosion in the use and the scope of medical imaging, fueled by advanced computing and visualization techniques. In my opinion, advanced, multimodal imaging and visualization techniques, ...
The next decades will see an explosion in the use and the scope of medical imaging, fueled by advanced computing and visualization techniques. In my opinion, advanced, multimodal imaging and visualization techniques, powered by new computational methods, will change the face of biology and medicine and provide comprehensive views of the human body in progressively greater depth and detail. As the resolution of imaging devices continue to increase, image sizes grow accordingly. Multi-modal and/or longitudinal imaging studies result in large-scale data sets requiring parallel computing and visualization. In this presentation, I will discuss the state-of-the-art in large-scale biomedical imaging and visualization research, present examples of their vital roles in neuroscience, neurosurgery, radiology, and biology and discuss future challenges.
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