Some radar image processing algorithms such as shape-from-shading are particularly compute-intensive and time consuming. If, in addition, a data set to be processed is large, then it may make sense to perform the proc...
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Some radar image processing algorithms such as shape-from-shading are particularly compute-intensive and time consuming. If, in addition, a data set to be processed is large, then it may make sense to perform the processing of images on multiple workstations or parallel processing systems. We have implemented shape-from-shading, stereo matching, resampling, gridding and visualization of terrain models in such a manner that they execute either on parallel machines or on clusters of workstations. We were motivated by the large image data set from NASA's Magellan mission to planet Venus, but received additional inspiration from the European Union's Center for Earth Observation program (CEO) and Austria's MISSION initiative for distributed processing of remote sensing images on remote workstations, using publicly accessible algorithms. We have developed a multi-processor approach that we denote as CDIP for Concurrent and Distributed Image Processing. The speedup for image processing tasks increases nearly linearly with the number of processors, be they on a parallel machine or arranged in a cluster of distributed workstations. Our approach adds benefits for users of complex image processing algorithms: the efforts for code porting and code maintenance are reduced and the necessity for specialized parallel processing hardware is eliminated.
In this paper we describe a trace analysis framework, from trace generation to visualization. It includes a unified tracing facility on IBMâ SPä systems, a self-defining interval file format, an API for fram...
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In this paper we describe a trace analysis framework, from trace generation to visualization. It includes a unified tracing facility on IBMâ SPä systems, a self-defining interval file format, an API for framework extensions, utilities for merging and statistics generation, and a visualization tool with preview and multiple time-space diagrams. The trace environment is extremely scalable, and combines MPI events with system activities in the same set of trace files, one for each SMP node. Since the amount of trace data may be very large, utilities are developed to convert and merge individual trace files into a self-defining interval trace file with multiple frame directories. The interval format allows the development of multiple time-space diagrams, such as thread-activity view, processor-activity view, etc., from the same interval file. A visualization tool, Jumpshot, is modified to visualize these views. A statistics utility is developed using the API, along with its graphics viewer.
In this paper we describe a trace analysis framework, from trace generation to visualization. It includes a unified tracing facility on IBM SP systems, a self-defining interval file format, an API for framework extens...
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ISBN:
(纸本)9780780398023
In this paper we describe a trace analysis framework, from trace generation to visualization. It includes a unified tracing facility on IBM SP systems, a self-defining interval file format, an API for framework extensions, utilities for merging and statistics generation, and a visualization tool with preview and multiple time-space diagrams. The trace environment is extremely scalable, and combines MPI events with system activities in the same set of trace files, one for each SMP node. Since the amount of trace data may be very large, utilities are developed to convert and merge individual trace files into a self-defining interval trace file with multiple frame directories. The interval format allows the development of multiple time-space diagrams, such as thread-activity view, processor-activity view, etc., from the same interval file. A visualization tool, Jumpshot, is modified to visualize these views. A statistics utility is developed using the API along with its graphics viewer.
The proceedings contain 12 papers. The topics discussed include: parallel lumigraph reconstruction;parallelvisualization of large-scale aerodynamics calculations: a case study on the Cray T3E;hybrid scheduling for pa...
ISBN:
(纸本)1581132379
The proceedings contain 12 papers. The topics discussed include: parallel lumigraph reconstruction;parallelvisualization of large-scale aerodynamics calculations: a case study on the Cray T3E;hybrid scheduling for parallel rendering using coherent ray tasks;exploiting frame coherence with the temporal depth buffer in a distributed computing environment;transparent distributed processing for rendering;web based collaborative visualization of distributed and parallel simulation;scalable distributed visualization using off-the-shelf components;and interactive volume segmentation with the PAVLOV Architecture.
We present a brute-force ray tracing system for interactive volume visualization. The system runs on a conventional (distributed) shared-memory multiprocessor machine. For each pixel we trace a ray through a volume to...
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We present a brute-force ray tracing system for interactive volume visualization. The system runs on a conventional (distributed) shared-memory multiprocessor machine. For each pixel we trace a ray through a volume to compute the color for that pixel. Although this method has high intrinsic computational cost, its simplicity and scalability make it ideal for largedatasets on current high-end parallel systems. To gain efficiency several optimizations are used including a volume bricking scheme and a shallow data hierarchy. These optimizations are used in three separate visualization algorithms: isosurfacing of rectilinear data, isosurfacing of unstructured data, and maximum-intensity projection on rectilinear data. The system runs interactively (i.e., several frames per second) on an SGI Reality Monster. The graphics capabilities of the Reality Monster are used only for display of the final color image.
Real-time visualization of large volume datasets demands high performance computation, pushing the storage, processing, and data communication requirements to the limits of current technology. General purpose parallel...
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Real-time visualization of large volume datasets demands high performance computation, pushing the storage, processing, and data communication requirements to the limits of current technology. General purpose parallel processors have been used to visualize moderate size datasets at interactive frame rates;however, the cost and size of these supercomputers inhibits the widespread use for real-time visualization. This paper surveys several special purpose architectures that seek to render volumes at interactive rates. These specialized visualization accelerators have cost, performance, and size advantages over parallel processors. All architectures implement ray casting using parallel and pipelined hardware. We introduce a new metric that normalizes performance to compare these architectures. The architectures included in this survey are VOGUE, VIRIM, Array Based Ray Casting, EM-Cube, and VIZARD II. We also discuss future applications of special purpose accelerators.
In this paper we introduce a scheme for static analysis that allows us to partition large geometric datasets at multiple levels of granularity to achieve both load balancing in parallel computations and minimal access...
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ISBN:
(纸本)1581132379
In this paper we introduce a scheme for static analysis that allows us to partition large geometric datasets at multiple levels of granularity to achieve both load balancing in parallel computations and minimal access to secondary memory in out-of-core computations. The idea is illustrated and fully exploited for the case of isosurface extraction, but extendible to a class of algorithms based on a small set of parameters and for which an appropriate static analysis can be performed.
parallelising ray tracing with a dataparallel approach allows rendering of arbitrarily large models, but the inherent load imbalances may lead to severe inefficiencies. To compensate for the uneven load distribution,...
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ISBN:
(纸本)1581132379
parallelising ray tracing with a dataparallel approach allows rendering of arbitrarily large models, but the inherent load imbalances may lead to severe inefficiencies. To compensate for the uneven load distribution, demand-driven tasks may be split off and scheduled to processors that are less busy. We propose a hybrid scheduling algorithm which brings tasks and data together according to coherence between rays. The amount of demand-driven versus dataparallel tasks is a function of the coherence between rays and the amount of imbalance in the basic data-parallel load. Processing power, communication and memory are three resources which should be evenly used. Our current implementation is assessed against these requirements, showing good scalability and very little communication at the cost of a slightly larger memory overhead.
作者：
Cavin, XavierAlonso, LaurentPaul, Jean-ClaudeLORIA
615 rue du Jardin Botanique BP 101 Villers-lès-Nancy CedexF-54602 France INRIA Lorraine
LORIA Is UMR 7503 LORIA CNRS Institut National Polytechnique de Lorraine INRIA Université Henri Poincaré Université Nancy 2 France
Recently, multi-processing has been shown to deliver good performance in rendering. However, in some applications, processors spend too much time executing tasks that could be more efficiently done through intensive u...
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ISBN:
(纸本)1581132379
Recently, multi-processing has been shown to deliver good performance in rendering. However, in some applications, processors spend too much time executing tasks that could be more efficiently done through intensive use of new graphics hardware. We present in this paper a novel solution combining multi-processing and advanced graphics hardware, where graphics pipelines are used both for classical visualization tasks and to advantageously perform geometric calculations while remaining computations are handled by multi-processors. The experiment is based on an implementation of a new parallel wavelet radiosity algorithm. The application is executed on the SGI Origin2000 connected to the SGI InfiniteReality2 rendering pipeline. A performance evaluation is presented. Keeping in mind that the approach can benefit all available workstations and super-computers, from small scale (p processors and n graphics pipeline) to large scale (p processors and n graphics pipelines), we highlight some important bottlenecks that impede performance. However, our results show that this approach could be a promising avenue for scientific and engineering simulation and visualization applications that need intensive geometric calculations.
作者：
Ma, KLNASA
Langley Res Ctr Inst Comp Applicat Sci & Engn Hampton VA 23681 USA
This paper describes work-in-progress on developing parallelvisualization strategies for 3D Adaptive Mesh Refinement (AMR) data. AMR is a simple and powerful tool for modeling many important scientific and engineerin...
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ISBN:
(纸本)0769500870
This paper describes work-in-progress on developing parallelvisualization strategies for 3D Adaptive Mesh Refinement (AMR) data. AMR is a simple and powerful tool for modeling many important scientific and engineering problems. However visualization tools for 3D AMR data are not generally available. Converting AMR data onto a uniform mesh would result in high storage requirements, and rendering the uniform-mesh data on an average graphics workstation can be painfully slow if not impossible. The adaptive nature of the embedded mesh demands sophisticated visualization calculations. In this work, we compare the performance and storage requirements of a parallel volume renderer for regular-mesh data with a new parallel renderer based on adaptive sampling. While both renderers can achieve interactive visualization, the new approach offers significant performance gains, as indicated by our experiments on the SGI/Cray T3E.
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