High Performance Data Mining: Scaling Algorithms, Applications and Systems

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In the long term, such an approach will bring the development of analytics algorithms to the next level: Furthermore, to meet the hardware trend— the architectures of emerging HPC systems are becoming inherently heterogeneous —our specific goal is to design algorithms for data analysis kernels accelerated on hybrid multi-node, multi-core HPC architectures comprised of a mix of GPUs, FPGA, and SSDs and develop their scalable implementations. Finally, the energy trend— performance-energy tradeoffs are becoming an essential part of the equation —drives the proposed advances in our performance-energy tradeoff analysis framework that would enable our data analysis kernels algorithms and software to be parameterized so that users can choose the right power-performance optimizations.

The apex of this proposal is a library of functions and software to accelerate data analytics, mining, knowledge discovery for large-scale scientific applications, thereby, increasing productivity of both scientists and the systems. The developed software will be released as open source for the benefits of the community in large. Moreover, students and postdoctoral associates on this project would be trained, readily available for DOE labs. Wes Bethel, Rob D. Ryne, and Arie Shoshani. In Proceedings of SuperComputing Conference Parallel processing Electronic computers Contents Machine derived contents note: Editorial; Yike Guo, R.

Srivastava, Eui-Hong Han, V. Notes "A special issue of Data mining and knowledge discovery, volume 3, no. View online Borrow Buy Freely available Show 0 more links Related resource Table of contents only at http: Set up My libraries How do I set up "My libraries"? These 5 locations in All: Open to the public ; University of Sydney Library. Open to the public. These 2 locations in New South Wales: