Choudhary et al 2015a - Revision history

Scipediacontent at 11:01, 22 January 2021

2021-01-22T11:01:05Z

Scipediacontent: Scipediacontent moved page Draft Content 204496063 to Choudhary et al 2015a

2020-10-26T14:43:03Z

Scipediacontent moved page Draft Content 204496063 to Choudhary et al 2015a

Scipediacontent: Created page with " == Abstract == This project developed a generic and optimized set of core data analytics functions. These functions organically consolidate a broad constellation of high per..."

2020-10-26T14:43:01Z

Created page with " == Abstract == This project developed a generic and optimized set of core data analytics functions. These functions organically consolidate a broad constellation of high per..."

New page

== 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.

Document type: Report

== Full document ==
<pdf>Media:Draft_Content_204496063-beopen499-7525-document.pdf</pdf>

== Original document ==

The different versions of the original document can be found in:

* [http://dx.doi.org/10.2172/1173060 http://dx.doi.org/10.2172/1173060]

* [http://pdfs.semanticscholar.org/65b4/3bdd59af7f3239c8678cdfa644255d8a6372.pdf http://pdfs.semanticscholar.org/65b4/3bdd59af7f3239c8678cdfa644255d8a6372.pdf]

* [https://www.osti.gov/scitech/servlets/purl/1173060 https://www.osti.gov/scitech/servlets/purl/1173060],[https://academic.microsoft.com/#/detail/2297568525 https://academic.microsoft.com/#/detail/2297568525]

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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.
@@ Line 18: / Line 13: @@
 * [http://pdfs.semanticscholar.org/65b4/3bdd59af7f3239c8678cdfa644255d8a6372.pdf http://pdfs.semanticscholar.org/65b4/3bdd59af7f3239c8678cdfa644255d8a6372.pdf]
-* [https://www.osti.gov/scitech/servlets/purl/1173060 https://www.osti.gov/scitech/servlets/purl/1173060],[https://academic.microsoft.com/#/detail/2297568525 https://academic.microsoft.com/#/detail/2297568525]
+* [https://www.osti.gov/servlets/purl/1173060 https://www.osti.gov/servlets/purl/1173060],

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