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Parallel volume visualization is of interest to a variety of application areas since current single-processor systems fall short in interactively rendering complex, large-sized datasets. This article presents a survey of volume-visualization methods on general-purpose parallel architectures, with special attention being paid to medical imaging applications. First, the various approaches to volume visualization are briefly discussed, followed by a description of relevant aspects of parallel architectures. Next, the implications of the various architectures are illustrated on the basis of a number of existing implementations of visualization algorithms on parallel architectures and their results. For parallel volume visualization, multiple instruction, multiple data (MIMD) architectures are found to be superior to single instruction, multiple data (SIMD) architectures. The latter type suffers from a lack of performance as well as flexibility. For most applications of interactive volume visualization, including the important area of medical imaging, shared memory MIMD architectures are preferred over distributed memory MIMD architectures. The ease of programming of shared memory architectures allows existing algorithms to be readily implemented without loss of performance or flexibility. |
