Abstract: A method, computer readable medium, and system are disclosed for supersampling a large-scale and disjoined data set. The data set may include point cloud, voxel, or polygonal mesh data. The data set may be rendered using a distributed, sort-last rendering system that includes a plurality of rendering nodes and one or more compositing nodes. The method includes the steps of receiving graphics data at a plurality of rendering nodes, rendering at least a portion of the graphics data by one or more rendering nodes to produce multi-sample image data, encoding the multi-sample image data using a difference encoding technique, and transmitting the encoded multi-sample image data to a compositing node. The multi-sample image data comprises a plurality of values per pixel of a target image corresponding to a plurality of sample locations defined for each pixel of the target image.

Abstract: A method, computer readable medium, and system are disclosed for supersampling a large-scale and disjoined data set. The data set may include point cloud, voxel, or polygonal mesh data. The data set may be rendered using a distributed, sort-last rendering system that includes a plurality of rendering nodes and one or more compositing nodes. The method includes the steps of receiving graphics data at a plurality of rendering nodes, rendering at least a portion of the graphics data by one or more rendering nodes to produce multi-sample image data, encoding the multi-sample image data using a difference encoding technique, and transmitting the encoded multi-sample image data to a compositing node. The multi-sample image data comprises a plurality of values per pixel of a target image corresponding to a plurality of sample locations defined for each pixel of the target image.

Abstract: An asynchronous computing and rendering system includes a data storage unit that provides storage for processing a large-scale data set organized in accordance to data subregions and a computing cluster containing a parallel plurality of asynchronous computing machines that provide compute results based on the data subregions. The asynchronous computing and rendering system also includes a rendering cluster containing a parallel multiplicity of asynchronous rendering machines coupled to the asynchronous computing machines, wherein each rendering machine renders a subset of the data subregions. Additionally, the asynchronous computing and rendering system includes a data interpretation platform coupled to the asynchronous rendering machines that provides user interaction and rendered viewing capabilities for the large-scale data set. An asynchronous computing and rendering method is also provided.

Abstract: An asynchronous computing and rendering system includes a data storage unit that provides storage for processing a large-scale data set organized in accordance to data subregions and a computing cluster containing a parallel plurality of asynchronous computing machines that provide compute results based on the data subregions. The asynchronous computing and rendering system also includes a rendering cluster containing a parallel multiplicity of asynchronous rendering machines coupled to the asynchronous computing machines, wherein each rendering machine renders a subset of the data subregions. Additionally, the asynchronous computing and rendering system includes a data interpretation platform coupled to the asynchronous rendering machines that provides user interaction and rendered viewing capabilities for the large-scale data set. An asynchronous computing and rendering method is also provided.