Scalable and Power Efficient Data Analytics for Hybrid Exascale Systems
- Northwestern Univ., Evanston, IL (United States); Northwestern University
- North Carolina State Univ., Raleigh, NC (United States); Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- Northwestern Univ., Evanston, IL (United States)
This project developed a generic and optimized set of core data analytics functions. These functions organically consolidate a broad constellation of high performance analytical pipelines. As the architectures of emerging HPC systems become inherently heterogeneous, there is a need to design algorithms for data analysis kernels accelerated on hybrid multi-node, multi-core HPC architectures comprised of a mix of CPUs, GPUs, and SSDs. Furthermore, the power-aware trend drives the advances in our performance-energy tradeoff analysis framework which enables our data analysis kernels algorithms and software to be parameterized so that users can choose the right power-performance optimizations.
- Research Organization:
- Northwestern University, Evanston, IL (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)
- DOE Contract Number:
- SC0005340
- OSTI ID:
- 1173060
- Report Number(s):
- DOE-NWU--0005340
- Country of Publication:
- United States
- Language:
- English
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