  The objective of our research is to demonstrate the practical usage and
orders of magnitude speedup of real-world applications by using alternative
technologies to support high performance computing. Currently, the main barrier
to the widespread adoption of this technology is the lack of development tools
and case studies that typically impede non-specialists that might otherwise
develop applications that could leverage these technologies. By partnering with
the Innovative Systems Laboratory at the National Center for Supercomputing, we
have obtained access to several novel technologies, including several
Field-Programmable Gate Array (FPGA) systems, NVidia Graphics Processing Units
(GPUs), and the STI Cell BE platform. Our goal is to not only demonstrate the
capabilities of these systems, but to also serve as guides for others to follow
in our path. To date, we have explored the efficacy of the SRC-6 MAP-C and
MAP-E and SGI RASC Athena and RC100 reconfigurable computing platforms in
supporting a two-point correlation function which is used in a number of
different scientific domains. In a brute force test, the FPGA based
single-processor system has achieved an almost two orders of magnitude speedup
over a single-processor CPU system. We are now developing implementations of
this algorithm on other platforms, including one using a GPU. Given the
considerable efforts of the cosmology community in optimizing these classes of
algorithms, we are currently working to implement an optimized version of the
basic family of correlation functions by using tree-based data structures.
Finally, we are also exploring other algorithms, such as instance-based
classifiers, power spectrum estimators, and higher-order correlation functions
that are also commonly used in a wide range of scientific disciplines.
