In discussions of ASCI, the high-profile procurements of large computers frequently figure prominently. However, from the outset of the ASCI program, applications have been recognized as the driver. These applications...
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ISBN:
(纸本)9780818681172
In discussions of ASCI, the high-profile procurements of large computers frequently figure prominently. However, from the outset of the ASCI program, applications have been recognized as the driver. These applications feature complex, multi-physics simulations of natural phenomena that generate massive data sets as output. As we have moved from computing systems dominated by parallel vector processing to massively parallel processing we have designed new applications from the ground up to take advantage of the new capabilities. Early payoffs from this effort include running problems that are one to two orders of magnitude larger than any we have been able to run in the past. With these larger problems, we are begining the computational exploration of domains in physics, chemistry and engineering that were previously closed. As we write these codes, issues associated with languages, debuggers and visualization tools have quickly risen to the surface. The process of running large problems has strained the computational infrastructure almost to the breaking point but indicates the direction for future work.
This paper presents a strategy for efficiently rendering time-varying volume data on a distributed-memory parallel computer. Visualizing time-varying volume data take both large storage space and long computation time...
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ISBN:
(纸本)0818682596
This paper presents a strategy for efficiently rendering time-varying volume data on a distributed-memory parallel computer. Visualizing time-varying volume data take both large storage space and long computation time. Instead of employing all processors to render one volume at a time, a pipelined rendering approach partitions processors into groups so that multiple volumes can be rendered concurrently. The overall rendering time is greatly minimized because rendering is overlapped with I/O required to load the volume data sets. Moreover, parallelization overhead may be reduced as a result of partitioning the processors. We modify an existing parallel volume renderer to exploit various levels of rendering parallelism and to study how the partitioning of processors may lead to optimal rendering performance. We find that two factors affecting the overall execution time are resource utilization efficiency and pipeline startup latency. The optimal partitioning configuration is the one that balances these two factors. Tests on Intel Paragon computers show that in general optimal partitionings do exist for a given rendering task and result in 40-50% saving in overall rendering time.
JPL's Remote Interactive visualization and Analysis System (RIVA) is described in detail. The RIVA system integrates workstation graphics, massively parallel computing technology, and gigabit communication network...
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JPL's Remote Interactive visualization and Analysis System (RIVA) is described in detail. The RIVA system integrates workstation graphics, massively parallel computing technology, and gigabit communication networks to provide a flexible interactive environment for scientific data perusal, analysis, and visualization. RIVA's kernel is a highly scalable parallel perspective renderer tailored especially for the demands of largedatasets beyond the sensible reach of workstations. Early experience with using RIVA to interactively explore and process multivariate, multiresolution datasets is reported;several examples using data from a variety of remote sensing instruments are discussed in detail and the results shown. Particular attention is placed on describing the algorithmic details of RIVA's parallel renderer kernel, with emphasis on the key aspects of achieving the algorithm's overall scalability. The paper summarizes the performance achieved for machine sizes up to more than 500 nodes and for initial input image/terrain bases in the 2 Gbyte range.
A motorized mechanical setup has been designed to provide a series of two-dimensional (2-D) parallel echographic slices of the breast from the regular translation of a linear phased array transducer, During the acquis...
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A motorized mechanical setup has been designed to provide a series of two-dimensional (2-D) parallel echographic slices of the breast from the regular translation of a linear phased array transducer, During the acquisition step, the breast is compressed between a plane support and a plexiglass plate to avoid breast motions. A stereoscopic computer graphic method has been developed for the visualization of such three-dimensional (3-D) ultrasonic data. Experiments on an agar gel phantom and on in vivo female breasts have been performed.
This paper presents a parallel volume rendering algorithm that can render a 256 x 256 x 225 voxel medical data set at over 15 Hz and a 512 x 512 x 334 voxel data set at over 7 Hz on a 32-processor Silicon graphics Cha...
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This paper presents a parallel volume rendering algorithm that can render a 256 x 256 x 225 voxel medical data set at over 15 Hz and a 512 x 512 x 334 voxel data set at over 7 Hz on a 32-processor Silicon graphics Challenge. The algorithm achieves these results by minimizing each of the three components of execution time: computation time, synchronization time, and data communication time. Computation time is low because the parallel algorithm is based on the recently-reported shear-warp serial volume rendering algorithm which is over five times faster than previous serial algorithms. The algorithm uses run-length encoding to exploit coherence and an efficient volume traversal to reduce overhead. Synchronization time is minimized by using dynamic load balancing and a task partition that minimizes synchronization events. data communication costs are low because the algorithm is implemented for shared-memory multiprocessors, a class of machines with hardware support for low-latency fine-grain communication and hardware caching to hide latency. We draw two conclusions from our implementation. First, we find that on shared-memory architectures data redistribution and communication costs do not dominate rendering time. Second, we find that cache locality requirements impose a limit on parallelism in volume rendering algorithms. Specifically, our results indicate that shared-memory machines with hundreds of processors would be useful only for rendering very largedata sets.
Many business datavisualization applications involve largedatabases with dozens of fields and millions of rows. Interactive visualization of these databases is difficult because of the large amount of data involved....
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Many business datavisualization applications involve largedatabases with dozens of fields and millions of rows. Interactive visualization of these databases is difficult because of the large amount of data involved. We present a method summarizing largedatabases which is well-suited to interactive visualization. We illustrate this with a visualization tool for the domain of call billing data.
We present a new multiresolution visualization design which allows a user to control the physical data resolution as well as the logical display resolution of multivariate data. A system prototype is described which u...
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We present a new multiresolution visualization design which allows a user to control the physical data resolution as well as the logical display resolution of multivariate data. A system prototype is described which uses the HyperSlice representation. The notion of space projection in multivariate data is introduced. This process is coupled with wavelets to form a powerful tool for very largedatavisualization.
The recent years have seen rapid advancement towards viewing the Earth as an integrated system. This means that we have come to understand the interdependence of the major planetary subsystems - atmosphere, biosphere,...
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The recent years have seen rapid advancement towards viewing the Earth as an integrated system. This means that we have come to understand the interdependence of the major planetary subsystems - atmosphere, biosphere, oceans and the deep earth interior - on a large range of time and length scales. One of the longest time scales of the planet is imposed by solid state convection within the silicate Earth mantle. Mantle convection modeling, and other earth science modeling efforts, now are producing simulation data on grids that are large enough to strain the memory and processing power of even the largest high-end graphics workstations. Another alternative is to use parallelvisualization tools running on the massively parallel computers that generated the data. This is the approach that we have taken for the visualization of mantle convection simulation data.
Understanding and interpreting largedata sets is an important but challenging operation in many technical disciplines. Computer visualization has become a valuable tool to help portray characteristics of largedata s...
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Understanding and interpreting largedata sets is an important but challenging operation in many technical disciplines. Computer visualization has become a valuable tool to help portray characteristics of largedata sets. In software visualization, illustrating the operation of very large programs or programs working on very largedata sets has remained one of the key open problems. We introduce an approach that uses semantic zooming to depict large program executions. Our method utilizes abstract, clustered graphics to portray program operations on the entire data set. Then, by interacting with the presentation, a viewer can zoom in to examine details and individual values. At this "magnified" level, the presentation adjusts to reflect displays common in existing algorithm animation and program visualization systems.
We present Cube-4, a special-purpose volume rendering architecture that is capable of rendering high-resolution (e.g., 1024/sup 3/) datasets at 30 frames per second. The underlying algorithm, called slice-parallel ray...
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We present Cube-4, a special-purpose volume rendering architecture that is capable of rendering high-resolution (e.g., 1024/sup 3/) datasets at 30 frames per second. The underlying algorithm, called slice-parallel ray-casting, uses tri-linear interpolation of samples between data slices for parallel and perspective projections. The architecture uses a distributed interleaved memory, several parallel processing pipelines, and an innovative paralleldata flow scheme that requires no global communication, except at the pixel level. This leads to local, fixed bandwidth interconnections and has the benefits of high memory bandwidth, real-time data input, modularity, and scalability. We have simulated the architecture and have implemented a working prototype of the complete hardware on a configurable custom hardware machine. Our results indicate true real-time performance for high-resolution datasets and linear scalability of performance with the number of processing pipelines.
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