Client + Cloud: Evaluating Seamless Architectures for Visual Data Analytics in the Ocean Sciences
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-one-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 the tradeoffs and requirements, and evaluate their performance.