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Ahmed E. Ismail

dblp:42/7147 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2016
—ORCID · unresolved

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
High-performance computing · 87% Hardware accelerators and domain-specific architectures · 13%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Computational science and engineering · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computational science and engineering › computational chemistry › molecular simulation
molecular dynamics
0.212016
The vectorization of the tersoff multi-body potential: an exercise in performance portability · SC 2016
High-performance computing › performance engineering
performance portability
0.212016
The vectorization of the tersoff multi-body potential: an exercise in performance portability · SC 2016
High-performance computing › code optimization
vectorization
0.212016
The vectorization of the tersoff multi-body potential: an exercise in performance portability · SC 2016
Hardware accelerators and domain-specific architectures
accelerator offloading
0.112016
The vectorization of the tersoff multi-body potential: an exercise in performance portability · SC 2016

Methods — techniques the papers use, named apart from their topics

vectorization · 0.5performance portability · 0.5
YearPublicationVenuePosition
2016 The vectorization of the tersoff multi-body potential: an exercise in performance portability
abstract
Molecular dynamics simulations, an indispensable research tool in computational chemistry and materials science, consume a significant portion of the supercomputing cycles around the world. We focus on multi-body potentials and aim at achieving performance portability. Compared with well-studied pair potentials, multibody potentials deliver increased simulation accuracy but are too complex for effective compiler optimization. Because of this, achieving cross-platform performance remains an open question. By abstracting from target architecture and computing precision, we develop a vectorization scheme applicable to both CPUs and accelerators. We present results for the Tersoff potential within the molecular dynamics code LAMMPS on several architectures, demonstrating efficiency gains not only for computational kernels, but also for large-scale simulations. On a cluster of Intel Xeon Phi's, our optimized solver is between 3 and 5 times faster than the pure MPI reference.
Markus Höhnerbach, Ahmed E. Ismail, Paolo Bientinesi
SC2