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Science is becoming data-intensive, requiring new software architectures that can exploit resources at all scales: local GPUs for interactive visualization, server-side multi-core machines with fast processors and large memories, and scalable, pay-as-you-go cloud resources. Architectures that seamlessly and flexibly exploit all three platforms are largely unexplored. Informed by a long-term collaboration with ocean scientists, the authors articulate a suite of representative visual data analytics workflows and use them to design and implement a multi-tier immersive visualization system. They then analyze a variety of candidate architectures spanning all three platforms, articulate their tradeoffs and requirements, and evaluate their performance.
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