Article ID Journal Published Year Pages File Type
392023 Information Sciences 2015 11 Pages PDF
Abstract

With the current proliferation of multi-core processors and hardware acceleration, large-data processing methods are increasingly being modified for use in parallel and distributed computing environments. In this paper, we present a hybrid architecture for the visualization and processing of large-scale volumetric data. Various hardware environments and technologies are integrated in this architecture to perform interactive operations on very large volumetric datasets. All of the datasets are stored in a data center with a gigabit network environment. Time-consuming data-processing tasks are allocated to compute nodes connected to the same network, while the visualization and interaction operations are executed on a high-performance graphics workstation. OpenCL and OpenMP are used to implement volume rendering algorithms to accelerate visualization of a hierarchical volume data structure using GPUs and multi-core CPUs. Various out-of-core algorithms are also presented to process the large dataset directly. The experimental results indicate that the proposed hybrid architecture and methods are effective and efficient in processing and visualizing very large-volumetric datasets.

Related Topics
Physical Sciences and Engineering Computer Science Artificial Intelligence
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