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Upcoming and future astronomy research facilities will systematicallygenerate terabyte-sized data sets moving astronomy into the Petascale data era.While such facilities will provide astronomers with unprecedented levels ofaccuracy and coverage, the increases in dataset size and dimensionality willpose serious computational challenges for many current astronomy data analysisand visualization tools. With such data sizes, even simple data analysis tasks(e.g. calculating a histogram or computing data minimum/maximum) may not beachievable without access to a supercomputing facility.  To effectively handle such dataset sizes, which exceed today's single machinememory and processing limits, we present a framework that exploits thedistributed power of GPUs and many-core CPUs, with a goal of providing dataanalysis and visualizing tasks as a service for astronomers. By mixing sharedand distributed memory architectures, our framework effectively utilizes theunderlying hardware infrastructure handling both batched and real-time dataanalysis and visualization tasks. Offering such functionality as a service in a"software as a service" manner will reduce the total cost of ownership, providean easy to use tool to the wider astronomical community, and enable a moreoptimized utilization of the underlying hardware infrastructure.

Unleashing the Power of Distributed CPU/GPU Architectures: Massive Astronomical Data Analysis and Visualization case study