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31
Cots cluster-based sort-last rendering: Performance evaluation and pipelined implementation
- In Proceedings of IEEE Visualization
, 2005
"... Figure 1: Views of the head section (512x512x209) of the visible female CT data with 16 nodes (a space has been left between the subvolumes to highlight their boundaries). Using a 3 years old 32-node COTS cluster, a volume dataset can be rendered at constant 13 frames per second on a 1024 × 768 rend ..."
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Figure 1: Views of the head section (512x512x209) of the visible female CT data with 16 nodes (a space has been left between the subvolumes to highlight their boundaries). Using a 3 years old 32-node COTS cluster, a volume dataset can be rendered at constant 13 frames per second on a 1024 × 768 rendering area using 5 nodes. On a 1.5 years old, fully optimized, 5-node COTS cluster, the frame rate obtained for the same rendering area reaches constant 31 frames per second. We truly expect our future work, including further algorithm optimizations and hardware tuning on a modern PC cluster, to provide higher frame rates for bigger datasets (using more nodes) on larger rendering areas. Sort-last parallel rendering is an efficient technique to visualize huge datasets on COTS clusters. The dataset is subdivided and distributed across the cluster nodes. For every frame, each node renders a full resolution image of its data using its local GPU, and the images are composited together using a parallel image compositing algorithm. In this paper, we present a performance evaluation of standard sort-last parallel rendering methods and of the different improvements proposed in the literature. This evaluation is based on a detailed analysis of the different hardware and software components.
Equalizer: A Scalable Parallel Rendering Framework
- IEEE Trans. Visualization and Computer Graphics
, 2008
"... Abstract — Continuing improvements in CPU and GPU performances as well as increasing multi-core processor and cluster-based parallelism demand for flexible and scalable parallel rendering solutions that can exploit multipipe hardware accelerated graphics. In fact, to achieve interactive visualizatio ..."
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Cited by 30 (1 self)
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Abstract — Continuing improvements in CPU and GPU performances as well as increasing multi-core processor and cluster-based parallelism demand for flexible and scalable parallel rendering solutions that can exploit multipipe hardware accelerated graphics. In fact, to achieve interactive visualization, scalable rendering systems are essential to cope with the rapid growth of data sets. However, parallel rendering systems are non-trivial to develop and often only application specific implementations have been proposed. The task of developing a scalable parallel rendering framework is even more difficult if it should be generic to support various types of data and visualization applications, and at the same time work efficiently on a cluster with distributed graphics cards. In this paper we introduce a novel system called Equalizer, a toolkit for scalable parallel rendering based on OpenGL which pro-vides an application programming interface (API) to develop scalable graphics applications for a wide range of systems ranging from large distributed visualization clusters and multi-processor multipipe graphics systems to single-processor single-pipe desktop ma-chines. We describe the system architecture, the basic API, discuss its advantadges over previous approaches, present example configurations and usage scenarios as well as scalability results.
Massively parallel volume rendering using 2-3 swap image compositing
- In Proceedings of the 2008 ACM/IEEE Conference on Supercomputing
, 2008
"... The ever-increasing amounts of simulation data produced by scientists demand high-end parallel visualization capability. However, image compositing, which requires interprocessor communication, is often the bottleneck stage for parallel rendering of large volume data sets. Existing image compositing ..."
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Cited by 26 (6 self)
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The ever-increasing amounts of simulation data produced by scientists demand high-end parallel visualization capability. However, image compositing, which requires interprocessor communication, is often the bottleneck stage for parallel rendering of large volume data sets. Existing image compositing solutions either incur a large number of messages exchanged among processors (such as the direct send method), or limit the number of processors that can be effectively utilized (such as the binary swap method). We introduce a new image compositing algorithm, called 2-3 swap, which combines the flexibility of the direct send method and the optimality of the binary swap method. The 2-3 swap algorithm allows an arbitrary number of processors to be used for compositing, and fully utilizes all participating processors throughout the course of the compositing. We experiment with this image compositing solution on a supercomputer with thousands of processors, and demonstrate its great flexibility as well as scalability. 1.
A Multi-Layered Image Cache for Scientific Visualization
- In IEEE Symposium on Parallel and Large-Data Visualization and Graphics
, 2003
"... We introduce a multi-layered image cache system that is designed to work with a pool of rendering engines to facilitate a frame-less, asynchronous rendering environment for scientific visualization. Our system decouples the rendering from the display of imagery at many levels; it decouples render fr ..."
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Cited by 11 (0 self)
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We introduce a multi-layered image cache system that is designed to work with a pool of rendering engines to facilitate a frame-less, asynchronous rendering environment for scientific visualization. Our system decouples the rendering from the display of imagery at many levels; it decouples render frequency and resolution from display frequency and resolution; allows asynchronous transmission of imagery instead of the compute-send cycle of standard parallel systems; and allows local, incremental refinement of imagery without requiring all imagery to be re-rendered.
Hardware Assisted Multichannel Volume Rendering
- In Computer Graphics International
, 2003
"... We explore real time volume rendering of multichannel data for volumes with color and multi-modal information. We demonstrate volume rendering of the Visible Human Male color dataset and photo-realistic rendering of voxelized terrains, and achieve high quality visualizations. We render multi-modal v ..."
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Cited by 9 (2 self)
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We explore real time volume rendering of multichannel data for volumes with color and multi-modal information. We demonstrate volume rendering of the Visible Human Male color dataset and photo-realistic rendering of voxelized terrains, and achieve high quality visualizations. We render multi-modal volumes utilizing hardware programmability for accumulation level mixing, and use CT and MRI information as examples. We also use multi-board parallel/distributed rendering schemes for large datasets and investigate scalability issues. We employ the Volume-Pro 1000 for real time multichannel volume rendering. Our approach, however, is not hardware-specific and can use commodity texture hardware instead.
ABSTRACT The Equalizer Parallel Rendering Framework
"... Continuing improvements in CPU and GPU performances as well as increasing multi-core processor and cluster-based parallelism demand for scalable parallel rendering solutions that can exploit multipipe hardware accelerated graphics. In fact, to achieve interactive visualization, scalable rendering sy ..."
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Cited by 4 (0 self)
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Continuing improvements in CPU and GPU performances as well as increasing multi-core processor and cluster-based parallelism demand for scalable parallel rendering solutions that can exploit multipipe hardware accelerated graphics. In fact, to achieve interactive visualization, scalable rendering systems are essential to cope with the rapid growth of data sets. However, parallel rendering solutions are non-trivial to develop and often only application specific implementations have been proposed. The task of developing a scalable parallel rendering framework is even more difficult if it should be generic to support various types of data and visualization applications, and at the same time work efficiently on a cluster with distributed graphics cards. In this paper we introduce Equalizer, a toolkit for scalable parallel rendering based on OpenGL which provides an application programming interface (API) to develop scalable graphics applications for a wide range of systems ranging from large distributed visualization clusters and multi-processor multipipe graphics systems to single-processor single-pipe desktop machines. We describe the architecture of Equalizer, discuss its advantadges over previous approaches, present example configurations and usage scenarios as well as some scalability results.
Npu-based image compositing in a distributed visualization system
- IEEE Transactions on Visualization and Computer Graphics
, 2007
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Distributed Volume Rendering on a Visualization Cluster
"... We describe the rendering of massive volumes on a volume visualization cluster. We present our data distribution scheme and introduce an algorithm which reduces the memory requirement with no loss of accuracy. The volume is automatically cropped and partitioned into small volume blocks. The bounding ..."
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Cited by 3 (0 self)
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We describe the rendering of massive volumes on a volume visualization cluster. We present our data distribution scheme and introduce an algorithm which reduces the memory requirement with no loss of accuracy. The volume is automatically cropped and partitioned into small volume blocks. The bounding boxes of these volume blocks are used at run-time for flexible partitioning of the volume across the network. We present results of rendering the full Visible Male color dataset, seismic data, and several large micro-CT scanned fossil and teeth datasets. 1.
Interactive Visualization of the Largest Radioastronomy Cubes
"... 3D visualization is an important data analysis and knowledge discovery tool, however, interactive visualization of large 3D astronomical datasets poses a challenge for many existing data visualization packages. We present a solution to interactively visualize larger-than-memory 3D astronomical data ..."
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Cited by 3 (1 self)
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3D visualization is an important data analysis and knowledge discovery tool, however, interactive visualization of large 3D astronomical datasets poses a challenge for many existing data visualization packages. We present a solution to interactively visualize larger-than-memory 3D astronomical data cubes by utilizing a heterogeneous cluster of CPUs and GPUs. The system partitions the data volume into smaller sub-volumes that are distributed over the rendering workstations. A GPU-based ray casting volume rendering is performed to generate images for each sub-volume, which are composited to generate the whole volume output, and returned to the user. Datasets including the HI Parkes All Sky Survey (HIPASS- 12 GB) southern sky and the Galactic All Sky Survey (GASS- 26 GB) data cubes were used to demonstrate our framework’s performance. The framework can render the GASS data cube with a maximum render time < 0.3 second with 1024 × 1024 pixels output resolution using 3 rendering workstations and 8 GPUs. Our framework will scale to visualize larger datasets, even of Terabyte order, if proper hardware infrastructure is available.
Parallel Multi-PC Volume Rendering System
"... We present a parallel multi-PC volume rendering system using offthe -shelf commodity components. We exploit the hardware accelerated 3D texture mapping of the GeForce3 graphics processors to visualize volume datasets. The system consists of two parts. One is client interface running on Windows and t ..."
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Cited by 2 (0 self)
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We present a parallel multi-PC volume rendering system using offthe -shelf commodity components. We exploit the hardware accelerated 3D texture mapping of the GeForce3 graphics processors to visualize volume datasets. The system consists of two parts. One is client interface running on Windows and the other is the rendering server running on Linux. We also use the GeForce3 processor in the client interface to edit the transfer functions and color maps interactively using decimated datasets, which fit on a single machine. After the client interface requests high quality images from the rendering server, the rendering server uses multiple PCs to render the images within a second. Our implementation addresses the problem of the frame buffers lack of precision, which causes artifacts when sub-volumes are blended individually, then blended together. 1
