001     830547
005     20210129230534.0
020 _ _ |a 978-3-319-58943-5
020 _ _ |a 978-3-319-58943-5 (electronic)
024 7 _ |2 doi
|a 10.1007/978-3-319-58943-5_60
024 7 _ |2 ISSN
|a 0302-9743
024 7 _ |2 ISSN
|a 1611-3349
024 7 _ |2 Handle
|a 2128/14664
024 7 _ |a WOS:000529303100060
|2 WOS
037 _ _ |a FZJ-2017-04079
041 _ _ |a English
082 _ _ |a 004
100 1 _ |0 P:(DE-Juel1)156619
|a Baumeister, Paul F.
|b 0
|e Corresponding author
111 2 _ |a Euro-Par 2016 International Workshops
|c Grenoble
|d 2016-08-24 - 2016-08-26
|w France
245 _ _ |a Exploiting In-Memory Processing Capabilities for Density Functional Theory Applications
260 _ _ |a Cham
|b Springer International Publishing
|c 2017
295 1 0 |a Euro-Par 2016: Parallel Processing Workshops / Desprez, Frederic (Editor) ; Cham : Springer International Publishing, 2017, Chapter 60 ; ISSN: 0302-9743=1611-3349 ; ISBN: 978-3-319-58942-8=978-3-319-58943-5 ; doi:10.1007/978-3-319-58943-5
300 _ _ |a 750 - 762
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490 0 _ |a Lecture Notes in Computer Science
|v 10104
520 _ _ |a Processing-in-memory (PIM) is an approach to address the data transport challenge in future HPC architectures and various designs have been explored in the past. Despite, it remains unclear how scien- tific applications could efficiently exploit massively-parallel HPC archi- tectures integrating PIM modules. In this paper we address this question for material science applications for which we ported relevant kernels to the Active Memory Cube architecture developed by IBM Research.
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700 1 _ |0 P:(DE-Juel1)176815
|a Hater, Thorsten
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700 1 _ |0 P:(DE-Juel1)144441
|a Pleiter, Dirk
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700 1 _ |0 P:(DE-HGF)0
|a Boettiger, Hans
|b 3
700 1 _ |0 P:(DE-HGF)0
|a Maurer, Thilo
|b 4
700 1 _ |0 P:(DE-HGF)0
|a Brunheroto, Jose R.
|b 5
773 _ _ |a 10.1007/978-3-319-58943-5_60
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