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