One of the greatest challenges facing scientists doing large computation of vector fields in a distributed parallel setting is the need for optimal parallel algorithms for flowvisualization. To address this need, we ...
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ISBN:
(纸本)9781538668733
One of the greatest challenges facing scientists doing large computation of vector fields in a distributed parallel setting is the need for optimal parallel algorithms for flowvisualization. To address this need, we present a new flowvisualization method based on parallel 3D line integral convolution (LIC). Our approach uses the fact that 3D LIC only needs limited local information to design an embarrassingly parallel model with a trade-off between the additional memory cost of external cells and the time cost for communication. All data required for each process can be stored in either a local data block or the external cells which are sets of exterior data surrounding the local partition. One problem for parallel LIC is that equal domain size decomposition of the data cannot guarantee balanced parallel processes. To achieve a load-balanced visualization process, we repartition data using an estimate of the LIC computation time. In addition, to minimize the memory cost, we introduce a vector-driven external cell expansion method to reduce the required memory cost. We find that we can use fewer external cells with minimal loss of visual quality. We evaluate the performance of our visualization method by first comparing its parallel scalability with traditional integral field line visualization. Next, we compare our new partition method with other data partition methods to verify that the workload of our model is more balanced. Finally, we compare our external cell expansion method with a traditional layer-based external cell expansion method. Consequently, together with the new partition and external cell expansion methods, our parallel 3D LIC visualization proves to be an efficient and well-balanced parallel flowvisualization with limited extra memory cost and a large saving of communication time.
Existing texture-based 3D flowvisualization techniques, e.g., volume Line Integral Convolution (LTC), are either limited to steady flows or dependent on special-purpose graphics cards. In this paper we present a text...
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Existing texture-based 3D flowvisualization techniques, e.g., volume Line Integral Convolution (LTC), are either limited to steady flows or dependent on special-purpose graphics cards. In this paper we present a texture-based hardware-independent technique for time-varying volume flowvisualization. It is based on our Accelerated Unsteady flow LTC (AUFLIC) algorithm (Liu and Moorhead, 2005), which uses a flow-driven seeding strategy and a dynamic seeding controller to reuse pathlines in the value scattering process to achieve fast time-dependent flowvisualization with high temporal-spatial coherence. We extend AUFLIC to 3D scenarios for accelerated generation of volume flowtextures. To address occlusion, lack of depth cuing, and poor perception of flow directions within a dense volume, we employ magnitude-based transfer functions and cutting planes in volume rendering to clearly show the flow structure and the flow evolution.
flowvisualization has been a very attractive component of scientific visualization research for a long time. Usually very large multivariate datasets require processing. These datasets often consist of a large number...
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flowvisualization has been a very attractive component of scientific visualization research for a long time. Usually very large multivariate datasets require processing. These datasets often consist of a large number of sample locations and several time steps. The steadily increasing performance of computers has recently become a driving factor for a reemergence in flowvisualization research, especially in texture-based techniques. In this paper, dense, texture-based flow visualization techniques are discussed. This class of techniques attempts to provide a complete, dense representation of the flow field with high spatio-temporal coherency. An attempt of categorizing closely related solutions is incorporated and presented. Fundamentals are shortly addressed as well as advantages and disadvantages of the methods.
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