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

Developing and Deploying Advanced Algorithms to Novel Supercomputing Hardware