We present the design of a scalable parallel pathline construction method for visualizing large time-varying 3D vector fields. A 4D (i.e., time and the 3D spatial domain) representation of the vector field is introduced to make a timeaccurate depiction of the flow field. This representation also allows us to obtain pathlines through streamline tracing in the 4D space. Furthermore, a hierarchical representation of the 4D vector field, constructed by clustering the 4D field, makes possible interactive visualization of the flow field at different levels of abstraction. Based on this hierarchical representation, a data partitioning scheme is designed to achieve high parallel efficiency. We demonstrate the performance of parallel pathline visualization using data sets obtained from terascale flow simulations. This new capability will enable scientists to study their time-varying vector fields at the resolution and interactivity previously unavailable to them. ...

raditionally, parallel supercomputing has focused on the inner kernel of scientific simulations: the solver. The front and back ends of the simulation pipeline--problem description and interpretation of the output--have taken a back seat to the solver when it comes to attention paid to scalability and performance, and are often relegated to offline, sequential computation. As the largest simulations move beyond the realm of the terascale and into the petascale, this decomposition in tasks and platforms becomes increasingly untenable. We propose an end-to-end approach in which all simulation components—meshing, partitioning, solver, and visualization—are tightly coupled and execute in parallel with shared data structures and no intermediate I/O. We present our implementation of this new approach in the context of octree-based finite element simulation of earthquake ground motion. Performance evaluation on up to 2048 processors demonstrates the ability of the end-toend approach to overcome the scalability bottlenecks of the traditional approach. ...

The capability to visualize large volume datasets has applications in a myriad of scientic elds. This paper presents a large data visualization solution in the form of distributed, multiresolution, progressive processing. This solution reduces the problem of rendering a large volume data into many simple and independent problems that can be straightforwardly distributed to multiple computers. By completely decoupling rendering and display with image caching, we are able to maintain a high level of interactivity during exploration of the data, which is key to obtaining insights into the data ...

This paper presents two parallel I/O methods for the visualization of time-varying volume data in a high-performance computing environment. We discuss the interplay between the parallel renderer, I/O strategy, and file system, and show the results of our study on the performance of the I/O strategies with and without MPI parallel I/O support. The targeted application is earthquake modeling using a large 3D unstructured mesh consisting of one hundred millions cells ...

This paper presents a parallel visualization pipeline implemented at the Pittsburgh Supercomputing Center (PSC) for studying the largest earthquake simulation ever performed. The simulation employs 100 million hexahedral cells to model 3D seismic wave propagation of the 1994 Northridge earthquake. The time-varying dataset produced by the simulation requires terabytes of storage space. Our solution for visualizing such terascale simulations is based on a parallel adaptive rendering algorithm coupled with a new parallel I/O strategy which effectively reduces interframe delay by dedicating some processors to I/O and preprocessing tasks ...

This paper presents a parallel adaptive rendering algorithm and its performance for visualizing time-varying unstructured volume data generated from large-scale eathquake simulations. The objective is to visualize 3D seismic wave propagation generated from an 0.5 Hz simulation of the Northridge earthquake, which is the highest resolution volume... ...

Parallel volume rendering oers a feasible solution to the large data visualization problem by distributing both the data and rendering calculations among multiple computers connected by a network. In sort-last parallel volume rendering, each processor generates an image of its assigned subvolume, which is blended together with other images to derive the final image. Improving the efficiency of this compositing step, which requires interprocesssor communication, is the key to scalable, interactive rendering ...

This paper presents two new hardware-assisted rendering techniques developed for interactive visualization of the terascale data generated from numerical modeling of next generation accelerator designs. The first technique, based on a hybrid rendering approach, makes possible interactive exploration of large-scale particle data from particle beam dynamics modeling. The second technique, based on a compact texture-enhanced representation, exploits the advanced features of commodity graphics cards to achieve perceptually effective visualization of the very dense and complex ...

To seek a low-cost, extensible solution for the large-scale data visualization problem, a visual computing system is designed as a result of a collaboration between industry and government research laboratories in Japan, also with participation by researchers in U.S. This scalable system is a commodity PC cluster equipped with the VolumePro 500 volume graphics cards and a specially designed image compositing hardware ...

This paper presents an end-to-end, low-cost solution for visualizing time-varying volume data rendered on a parallel computer located at a remote site. Pipelining and careful grouping of processors are used to hide I/O time and to maximize processors utilization. Compression is used to significantly cut down the cost of transferring output images from the parallel computer to a display device through a wide-area network ...

This paper reports the performance of a parallel volume rendering algorithm for visualizing a largescale unstructured-grid dataset produced by a three-dimensional aerodynamics simulation. This dataset, containing over 18 million tetrahedra, allows us to extend our performance results to a problem which is more than 30 times larger than the one we examined previously. This high resolution dataset also allows us to see fine, threedimensional features in the flow field ...

We are developing techniques for massively parallel volume rendering, examining primarily the techniques of algorithms on general parallel computers. Research involves the use of the UCSC 4096 processor MasPar MP-2 and numerous workstations. Recent NSF funding shall provide a very high performance visualization platform on which we shall also do research. Important research topics are: what is the maximum performance attainable for volumetric visualization on any given parallel computer? What is the optimal algorithm for volume visualization for a general model of computation? Which interactivity features provide capabilities that allow new results to be discovered with volumetric data? ...

This paper discusses some aspects of the design of a data distributed, massively parallel volume rendering library for runtime visualization of parallel computational fluid dynamics simulations in a message-passing environment. Unlike the traditional scheme in which visualization is a postprocessing step, the rendering is done in place on each node processor. Computational scientists who run large-scale simulations on a massively parallel computer can thus perform interactive monitoring of their simulations ...

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This paper presents a divide-and-conquer ray-traced volume rendering algorithm and a parallel image compositing method, along with their implementation and performance on the Connection Machine CM-5, and networked workstations. This algorithm distributes both the data and the computations to individual processing units to achieve fast, high-quality rendering of high-resolution data. The volume data, once distributed, is left intact. The processing nodes perform local ray tracing of their subvolume concurrently ...