Maximum likelihood analysis is a powerful technique for inferring evolutionary histories from genetic sequence data. During the fall of 2003, an international team of computer scientists, biologists, and computer centers created a global grid to analyze the evolution of hexapods (arthropods with six legs). We created a global grid of computers using systems located in eight countries, spread across six continents (every continent but Antarctica). This work was done as part of the SC03 HPC Challenge, and this project was given an HPC Challenge award for the "Most Distributed Application." More importantly, the creation of this computing grid enabled investigation of important questions regarding the evolution of arthropods -research that would not have otherwise been undertaken. Grid computing will thus lead directly to new scientific insights.

Remote rendering is employed when the visualization task is too challenging for the hardware used to display a dataset or when it is too time consuming to transfer the complete dataset. Volume visualization with its dataset sizes growing with the 3rd power of their spatial resolution is such a task. Since remote rendering introduces additional sources of latency, its applicability to virtual environments is limited because of the required low delays from user action to displayed image. We counter these latencies with image-based rendering techniques: color image data along with additional depth information is warped, while new data has not been completely received. Using these approximate images, it is possible to decouple the cheap display phase from rendering. While depth values are trivially deduced for polygons, we contribute heuristics for volumetric datasets with varying transparency.

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