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A parallel preintegration volume rendering algorithm based on adaptive sampling is proposed in this paper to visualize large-scale scientific data effectively on distributed-memory parallel computers. The algorithm sets sampling points adaptively by detecting the extremal points of a data field along the rays, so it can grasp the data variation exactly. After the data field is sampled distributedly on CPU cores, the resulting sampling points are sorted by piecewise packing orderly sampling points and then composited along each ray using the preintegration technique. In the algorithm, a static load balancing scheme based on information entropy is also proposed to balance the loads of both data reading and ray sampling. In addition, a mixed logarithmic quantization scheme is suggested to quantize depth distance so as to shorten the preintegration table while preserving the rendering quality. It is demonstrated that the presented algorithm can show inner features in a data field clearly and achieve a rendering speedup ratio between 1.8 and 4.4, compared with the traditional parallel volume rendering algorithm.

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