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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. | ||
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* [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] |
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.
The different versions of the original document can be found in:
Published on 01/01/2015
Volume 2015, 2015
DOI: 10.2172/1173060
Licence: CC BY-NC-SA license
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