Scalable and Power Efficient Data Analytics for Hybrid Exascale Systems
- Northwestern Univ., Evanston, IL (United States)
- 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)
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 Univ., Evanston, IL (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
- DOE Contract Number:
- SC0005340
- OSTI ID:
- 1173060
- Report Number(s):
- DOE-NWU-0005340
- Country of Publication:
- United States
- Language:
- English
Similar Records
Data Locality Enhancement of Dynamic Simulations for Exascale Computing (Final Report)
Efficient Machine Learning Approach for Optimizing Scientific Computing Applications on Emerging HPC Architectures
A massively parallel and scalable multi-CPU material point method
Technical Report
·
Fri Nov 29 00:00:00 EST 2019
·
OSTI ID:1173060
Efficient Machine Learning Approach for Optimizing Scientific Computing Applications on Emerging HPC Architectures
Thesis/Dissertation
·
Mon May 01 00:00:00 EDT 2017
·
OSTI ID:1173060
A massively parallel and scalable multi-CPU material point method
Conference
·
Wed Jul 01 00:00:00 EDT 2020
·
OSTI ID:1173060
+7 more