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Scalable heterogeneous execution of a coupled-cluster model with perturbative triples

Authors
Kim, J.Panyala, A.Peng, B.Kowalski, K.Sadayappan, P.Krishnamoorthy, S.
Issue Date
Nov-2020
Publisher
IEEE Computer Society
Citation
International Conference for High Performance Computing, Networking, Storage and Analysis, SC
Journal Title
International Conference for High Performance Computing, Networking, Storage and Analysis, SC
URI
https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/63152
DOI
10.1109/SC41405.2020.00083
ISSN
2167-4329
Abstract
The CCSD(T) coupled-cluster model with perturbative triples is considered a gold standard for computational modeling of the correlated behavior of electrons in molecular systems. A fundamental constraint is the relatively small global-memory capacity in GPUs compared to the main-memory capacity on host nodes, necessitating relatively smaller tile sizes for high-dimensional tensor contractions in NWChem's GPU-accelerated implementation of the CCSD(T) method. A coordinated redesign is described to address this limitation and associated data movement overheads, including a novel fused GPU kernel for a set of tensor contractions, along with inter-node communication optimization and data caching. The new implementation of GPU-accelerated CCSD(T) improves overall performance by ;3.4 ×. Finally, we discuss the trade-offs in using this fused algorithm on current and future supercomputing platforms. © 2020 IEEE.
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소프트웨어대학 (소프트웨어학부)
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