Koning A H, Zuiderveld K J, Viergever M A
3D Computer Vision Research Group, AZU, Utrecht, The Netherlands.
Med Inform (Lond). 1994 Jul-Sep;19(3):283-93. doi: 10.3109/14639239409025333.
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.
由于当前的单处理器系统在交互式渲染复杂的大型数据集方面存在不足，并行体可视化在各种应用领域中受到关注。本文对通用并行架构上的体可视化方法进行了综述，特别关注医学成像应用。首先，简要讨论了体可视化的各种方法，随后描述了并行架构的相关方面。接下来，基于并行架构上可视化算法的一些现有实现及其结果，阐述了各种架构的影响。对于并行体可视化，发现多指令多数据（MIMD）架构优于单指令多数据（SIMD）架构。后一种类型存在性能和灵活性不足的问题。对于交互式体可视化的大多数应用，包括医学成像这一重要领域，共享内存MIMD架构优于分布式内存MIMD架构。共享内存架构易于编程，使得现有算法能够轻松实现，而不会损失性能或灵活性。
