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A data-centric approach to extreme-scale ab initio dissipative quantum transport simulations

Authors:

Alexandros Nikolaos Ziogas,

Guillermo Indalecio Fernández,

Timo Schneider,

Mathieu Luisier,

Torsten HoeflerAuthors Info & Claims

SC '19: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis

Article No.: 1, Pages 1 - 13

https://doi.org/10.1145/3295500.3357156

Published: 17 November 2019 Publication History

Abstract

The computational efficiency of a state of the art ab initio quantum transport (QT) solver, capable of revealing the coupled electrothermal properties of atomically-resolved nano-transistors, has been improved by up to two orders of magnitude through a data centric reorganization of the application. The approach yields coarse- and fine-grained data-movement characteristics that can be used for performance and communication modeling, communication-avoidance, and dataflow transformations. The resulting code has been tuned for two top-6 hybrid supercomputers, reaching a sustained performance of 85.45 Pflop/s on 4,560 nodes of Summit (42.55% of the peak) in double precision, and 90.89 Pflop/s in mixed precision. These computational achievements enable the restructured QT simulator to treat realistic nanoelectronic devices made of more than 10,000 atoms within a 14x shorter duration than the original code needs to handle a system with 1,000 atoms, on the same number of CPUs/GPUs and with the same physical accuracy.

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Digital Library

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A. N. Ziogas, T. Ben-Nun, G. Indalecio Fernandez, T. Schneider, M. Luisier, and T. Hoefler. 2019. Optimizing the Data Movement in Quantum Transport Simulations via Data-Centric Parallel Programming. In Proc. Int'l Conference for High Performance Computing, Networking, Storage and Analysis.

Cited By

Radamson HMiao YZhou ZWu ZKong ZGao JYang HRen YZhang YShi JXiang JCui HLu BLi JLiu JLin HXu HLi MCao JHe CDuan XZhao XSu JDu YYu JWu YJiang MLiang DLi BDong YWang G(2024)CMOS Scaling for the 5 nm Node and Beyond: Device, Process and TechnologyNanomaterials10.3390/nano1410083714:10(837)Online publication date: 9-May-2024
https://doi.org/10.3390/nano14100837
Zhao YLiu FMa WLi HPeng YWang C(2023)MFFT: A GPU Accelerated Highly Efficient Mixed-Precision Large-Scale FFT FrameworkACM Transactions on Architecture and Code Optimization10.1145/360514820:3(1-23)Online publication date: 22-Jul-2023
https://dl.acm.org/doi/10.1145/3605148
Schaad PSchneider TBen-Nun TCalotoiu AZiogas AHoefler TMohror KArnold DBadia R(2023)FuzzyFlow: Leveraging Dataflow To Find and Squash Program Optimization BugsProceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis10.1145/3581784.3613214(1-15)Online publication date: 12-Nov-2023
https://dl.acm.org/doi/10.1145/3581784.3613214
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Index Terms

A data-centric approach to extreme-scale ab initio dissipative quantum transport simulations
1. Computing methodologies
  1. Modeling and simulation
    1. Simulation types and techniques
      1. Massively parallel and high-performance simulations
      2. Quantum mechanic simulation
  2. Parallel computing methodologies

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cover image ACM Conferences

SC '19: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis

November 2019

1921 pages

ISBN:9781450362290

DOI:10.1145/3295500

General Chair:
Michela Taufer,
Program Chairs:
Pavan Balaji,
Antonio J. Peña

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 17 November 2019

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Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
Horizon 2020 Framework Programme

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SC '19

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SIGHPC

SC '19: The International Conference for High Performance Computing, Networking, Storage, and Analysis

November 17 - 19, 2019

Colorado, Denver

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Cited By

Radamson HMiao YZhou ZWu ZKong ZGao JYang HRen YZhang YShi JXiang JCui HLu BLi JLiu JLin HXu HLi MCao JHe CDuan XZhao XSu JDu YYu JWu YJiang MLiang DLi BDong YWang G(2024)CMOS Scaling for the 5 nm Node and Beyond: Device, Process and TechnologyNanomaterials10.3390/nano1410083714:10(837)Online publication date: 9-May-2024
https://doi.org/10.3390/nano14100837
Zhao YLiu FMa WLi HPeng YWang C(2023)MFFT: A GPU Accelerated Highly Efficient Mixed-Precision Large-Scale FFT FrameworkACM Transactions on Architecture and Code Optimization10.1145/360514820:3(1-23)Online publication date: 22-Jul-2023
https://dl.acm.org/doi/10.1145/3605148
Schaad PSchneider TBen-Nun TCalotoiu AZiogas AHoefler TMohror KArnold DBadia R(2023)FuzzyFlow: Leveraging Dataflow To Find and Squash Program Optimization BugsProceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis10.1145/3581784.3613214(1-15)Online publication date: 12-Nov-2023
https://dl.acm.org/doi/10.1145/3581784.3613214
Ben-Nun TAtes BCalotoiu AHoefler TDubach CBruening DHardekopf B(2023)Bridging Control-Centric and Data-Centric OptimizationProceedings of the 21st ACM/IEEE International Symposium on Code Generation and Optimization10.1145/3579990.3580018(173-185)Online publication date: 17-Feb-2023
https://dl.acm.org/doi/10.1145/3579990.3580018
Andersson MMarkidis S(2023)A Case Study on DaCe Portability & Performance for Batched Discrete Fourier TransformsProceedings of the International Conference on High Performance Computing in Asia-Pacific Region10.1145/3578178.3578239(55-63)Online publication date: 27-Feb-2023
https://dl.acm.org/doi/10.1145/3578178.3578239
Lee YCao JLuisier M(2023)Atomistic Simulation of Nanoscale DevicesIEEE Nanotechnology Magazine10.1109/MNANO.2023.327896817:4(4-14)Online publication date: Aug-2023
https://doi.org/10.1109/MNANO.2023.3278968
Shipman GPruet JDaniel DDolence JGrider GHaines BHungerford APoole SRandles TSwaminarayan SWerner C(2023)The Future of HPC in Nuclear SecurityIEEE Internet Computing10.1109/MIC.2022.322903727:1(16-23)Online publication date: 1-Jan-2023
https://doi.org/10.1109/MIC.2022.3229037
Weßner JBerlich RSchwarz KLutz M(2023)Parametric Optimization on HPC Clusters with GenevaComputing and Software for Big Science10.1007/s41781-023-00098-67:1Online publication date: 21-Apr-2023
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Ma ZJin YTang SWang HXue WZhai JZheng W(2023)Unified Programming Models for Heterogeneous High-Performance ComputersJournal of Computer Science and Technology10.1007/s11390-023-2888-438:1(211-218)Online publication date: 31-Jan-2023
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Li RDeng LDai ZZhang JLiu JLiu G(2023)A Data-Centric Approach for Efficient and Scalable CFD Implementation on Multi-GPUs ClustersParallel and Distributed Computing, Applications and Technologies10.1007/978-981-99-8211-0_10(93-104)Online publication date: 29-Nov-2023
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