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Long Wang 0008

dblp:68/4459-8 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2011
—ORCID · unresolved

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

Systems, architecture and hardware · 1 · 1 first-author

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 · 61% GPUs and heterogeneous computing · 30% Parallel and multicore computing · 9%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
High-performance computing › quantum chemistry
density functional theory		 0.112011
Large scale plane wave pseudopotential density functional theory calculations on GPU clusters · SC 2011
GPUs and heterogeneous computing
GPU-accelerated scientific computing		 0.112011
Large scale plane wave pseudopotential density functional theory calculations on GPU clusters · SC 2011
High-performance computing
scientific computing systems		 0.112011
Large scale plane wave pseudopotential density functional theory calculations on GPU clusters · SC 2011

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

reciprocal-space and band-index parallelization · 0.1plane wave pseudopotential · 0.1
YearPublicationVenuePosition
2011 Large scale plane wave pseudopotential density functional theory calculations on GPU clusters
abstract
In this work, we present our implementation of the density functional theory (DFT) plane wave pseudopotential (PWP) calculations on GPU clusters. This GPU version is developed based on a CPU DFT-PWP code: PEtot, which can calculate up to a thousand atoms on thousands of processors. Our test indicates that the GPU version can have a ~10 times speed-up over the CPU version. A detail analysis of the speed-up and the scaling on the number of CPU/GPU computing units (up to 256) are presented. The success of our speed-up relies on the adoption a hybrid reciprocal-space and band-index parallelization scheme. As far as we know, this is the first GPU DFT-PWP code scalable to large number of CPU/GPU computing units. We also outlined the future work, and what is needed to further increase the computational speed by another factor of 10.
Long Wang 0008, Weile Jia, Weiguo Gao, Xuebin Chi, Lin-Wang Wang
 SC1