Exploiting In-Memory Processing Capabilities for Density Functional Theory Applications

Baumeister, Paul F.; Maurer, Thilo; Hater, Thorsten; Brunheroto, Jose R.; Boettiger, Hans; Pleiter, Dirk

doi:10.1007/978-3-319-58943-5_60

Contribution to a conference proceedings/Contribution to a book

FZJ-2017-04079

Exploiting In-Memory Processing Capabilities for Density Functional Theory Applications

Baumeister, P. F. (Corresponding author)FZJ* ; Hater, T. ; Pleiter, D.FZJ* ; Boettiger, H. ; Maurer, T. ; Brunheroto, J. R.

2017
Springer International Publishing Cham
ISBN: 978-3-319-58943-5, 978-3-319-58943-5 (electronic)

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
Euro-Par 2016 International Workshops, Grenoble, France, 24 Aug 2016 - 26 Aug 2016 Cham : Springer International Publishing, Lecture Notes in Computer Science 10104(60), 750 - 762 (2017) [10.1007/978-3-319-58943-5_60]

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Please use a persistent id in citations: http://hdl.handle.net/2128/14664 doi:10.1007/978-3-319-58943-5_60

Abstract: 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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