m (Scipediacontent moved page Draft Content 204496063 to Choudhary et al 2015a) |
|||
| Line 3: | Line 3: | ||
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. | 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. | ||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| Line 18: | Line 13: | ||
* [http://pdfs.semanticscholar.org/65b4/3bdd59af7f3239c8678cdfa644255d8a6372.pdf http://pdfs.semanticscholar.org/65b4/3bdd59af7f3239c8678cdfa644255d8a6372.pdf] | * [http://pdfs.semanticscholar.org/65b4/3bdd59af7f3239c8678cdfa644255d8a6372.pdf http://pdfs.semanticscholar.org/65b4/3bdd59af7f3239c8678cdfa644255d8a6372.pdf] | ||
| − | * [https://www.osti.gov | + | * [https://www.osti.gov/servlets/purl/1173060 https://www.osti.gov/servlets/purl/1173060], |
| + | : [https://academic.microsoft.com/#/detail/2297568525 https://academic.microsoft.com/#/detail/2297568525] | ||
Latest revision as of 13:01, 22 January 2021
Abstract
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.
Original document
The different versions of the original document can be found in:
Document information
Published on 01/01/2015
Volume 2015, 2015
DOI: 10.2172/1173060
Licence: CC BY-NC-SA license
Share this document
Keywords
claim authorship
Are you one of the authors of this document?