research-article

Optimizing the data movement in quantum transport simulations via data-centric parallel programming

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.: 78, Pages 1 - 17

https://doi.org/10.1145/3295500.3356200

Published: 17 November 2019 Publication History

Abstract

Designing efficient cooling systems for integrated circuits (ICs) relies on a deep understanding of the electro-thermal properties of transistors. To shed light on this issue in currently fabricated Fin-FETs, a quantum mechanical solver capable of revealing atomically-resolved electron and phonon transport phenomena from first-principles is required. In this paper, we consider a global, data-centric view of a state-of-the-art quantum transport simulator to optimize its execution on supercomputers. The approach yields coarse-and fine-grained data-movement characteristics, which are used for performance and communication modeling, communication-avoidance, and data-layout transformations. The transformations are tuned for the Piz Daint and Summit supercomputers, where each platform requires different caching and fusion strategies to perform optimally. The presented results make ab initio device simulation enter a new era, where nanostructures composed of over 10,000 atoms can be investigated at an unprecedented level of accuracy, paving the way for better heat management in next-generation ICs.

References

[1]

T. Ben-Nun, J. de Fine Licht, A. N. Ziogas, T. Schneider, and T. Hoefler. 2019. Stateful Dataflow Multigraphs: A Data-Centric Model for Performance Portability on Heterogeneous Architectures. In Proc. Int'l Conference for High Performance Computing, Networking, Storage and Analysis.

[2]

Mauro Calderara, Sascha Brück, Andreas Pedersen, Mohammad H. Bani-Hashemian, Joost VandeVondele, and Mathieu Luisier. 2015. Pushing Back the Limit of Ab-initio Quantum Transport Simulations on Hybrid Supercomputers. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC '15). ACM, New York, NY, USA, Article 3, 12 pages.

Digital Library

[3]

E. Carson, J. Demmel, L. Grigori, N. Knight, P. Koanantakool, O. Schwartz, and H. V. Simhadri. 2016. Write-Avoiding Algorithms. In 2016 IEEE International Parallel and Distributed Processing Symposium (IPDPS). 648--658.

[4]

Swiss National Supercomputing Centre. 2019. Piz Daint. https://www.cscs.ch/computers/piz-daint/

[5]

Supriyo Datta. 1995. Electronic Transport in Mesoscopic Systems. Cambridge University Press.

[6]

J. Demmel. 2013. Communication-avoiding algorithms for linear algebra and beyond. In 2013 IEEE 27th International Symposium on Parallel and Distributed Processing. 585--585.

Digital Library

[7]

Oak Ridge Leadership Computing Facility. 2019. Summit. https://www.olcf.ornl.gov/olcf-resources/compute-systems/summit/

[8]

Jaime Ferrer, Colin J Lambert, Víctor Manuel García-Suárez, D Zs Manrique, D Visontai, L Oroszlany, Rubén Rodríguez-Ferradás, Iain Grace, SWD Bailey, Katalin Gillemot, et al. 2014. GOLLUM: a next-generation simulation tool for electron, thermal and spin transport. New Journal of Physics 16, 9 (2014), 093029.

[9]

CEA Grenoble. 2013. TB_Sim. http://inac.cea.fr/LSim/TBSim/

[10]

Christoph W Groth, Michael Wimmer, Anton R Akhmerov, and Xavier Waintal. 2014. Kwant: a software package for quantum transport. New Journal of Physics 16, 6 (2014), 063065.

[11]

The Nanoelectronic Modeling Group and Gerhard Klimeck. 2018. NEMO5. https://engineering.purdue.edu/gekcogrp/software-projects/nemo5/

[12]

J. Izquierdo, A. Vega, L. C. Balbás, Daniel Sánchez-Portal, Javier Junquera, Emilio Artacho, Jose M. Soler, and Pablo Ordejón. 2000. Systematic ab initio study of the electronic and magnetic properties of different pure and mixed iron systems. Phys. Rev. B 61 (May 2000), 13639--13646. Issue 20.

[13]

W. Kohn and L. J. Sham. 1965. Self-Consistent Equations Including Exchange and Correlation Effects. Phys. Rev. 140 (Nov 1965), A1133--A1138. Issue 4A.

[14]

Roger Lake, Gerhard Klimeck, R. Chris Bowen, and Dejan Jovanovic. 1997. Single and multiband modeling of quantum electron transport through layered semiconductor devices. Journal of Applied Physics 81, 12 (1997), 7845--7869.

[15]

M. Luisier. 2010. A Parallel Implementation of Electron-Phonon Scattering in Nanoelectronic Devices up to 95k Cores. In SC '10: Proceedings of the 2010 ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis. 1--11.

Digital Library

[16]

Mathieu Luisier, Timothy B. Boykin, Gerhard Klimeck, and Wolfgang Fichtner. 2011. Atomistic Nanoelectronic Device Engineering with Sustained Performances Up to 1.44 PFlop/s. In Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis (SC '11). ACM, New York, NY, USA, Article 2, 11 pages.

Digital Library

[17]

Mathieu Luisier, Andreas Schenk, Wolfgang Fichtner, and Gerhard Klimeck. 2006. Atomistic simulation of nanowires in the sp³d⁵s^* tight-binding formalism: From boundary conditions to strain calculations. Phys. Rev. B 74 (Nov 2006), 205323. Issue 20.

[18]

NanoTCAD. 2017. ViDES. http://vides.nanotcad.com/vides/

[19]

Patrick McCormick. 2019. Yin & Yang: Hardware Heterogeneity & Software Productivity. Talk at SOS23 meeting, Asheville, NC.

[20]

Robert Pawlik. 2016. Current CPUs produce 4 times more heat than hot plates. https://www.cloudandheat.com/blog/current-cpus-produce-4-times-more-heat-than-hot-plates-future-performance-increases-only-possible-by-using-direct-water-cooling/

[21]

E. Pop, S. Sinha, and K. E. Goodson. 2006. Heat Generation and Transport in Nanometer-Scale Transistors. Proc. IEEE 94, 8 (Aug 2006), 1587--1601.

[22]

Christian Stieger, Aron Szabo, Teutë Bunjaku, and Mathieu Luisier. 2017. Ab-initio quantum transport simulation of self-heating in single-layer 2-D materials. Journal of Applied Physics 122, 4 (2017), 045708.

[23]

A. Svizhenko, M. P. Anantram, T. R. Govindan, B. Biegel, and R. Venugopal. 2002. Two-dimensional quantum mechanical modeling of nanotransistors. Journal of Applied Physics 91, 4 (2002), 2343--2354.

[24]

Synopsys. 2019. QuantumATK. https://www.synopsys.com/silicon/quantumatk.html

[25]

Atsushi Togo, Fumiyasu Oba, and Isao Tanaka. 2008. First-principles calculations of the ferroelastic transition between rutile-type and CaCl₂-type SiO₂ at high pressures. Phys. Rev. B 78 (Oct 2008), 134106. Issue 13.

[26]

D. Unat, A. Dubey, T. Hoefler, J. Shalf, M. Abraham, M. Bianco, B. L. Chamberlain, R. Cledat, H. C. Edwards, H. Finkel, K. Fuerlinger, F. Hannig, E. Jeannot, A. Kamil, J. Keasler, P. H. J. Kelly, V. Leung, H. Ltaief, N. Maruyama, C. J. Newburn, and M. Pericás. 2017. Trends in Data Locality Abstractions for HPC Systems. IEEE Transactions on Parallel and Distributed Systems 28, 10 (Oct 2017), 3007--3020.

[27]

Joost VandeVondele, Matthias Krack, Fawzi Mohamed, Michele Parrinello, Thomas Chassaing, and Jürg Hutter. 2005. Quickstep: Fast and accurate density functional calculations using a mixed Gaussian and plane waves approach. Computer Physics Communications 167, 2 (2005), 103--128.

[28]

J. Wei. 2008. Challenges in Cooling Design of CPU Packages for High-Performance Servers. Heat Transfer Engineering 29, 2 (2008), 178--187.

[29]

A. N. Ziogas, T. Ben-Nun, G. Indalecio Fernandez, T. Schneider, M. Luisier, and T. Hoefler. 2019. A Data-Centric Approach to Extreme-Scale Ab initio Dissipative Quantum Transport Simulations. In Proc. Int'l Conference for High Performance Computing, Networking, Storage and Analysis.

Cited By

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
https://doi.org/10.1007/978-981-99-8211-0_10
Rausch OBen-Nun TDryden NIvanov ALi SHoefler TRauchwerger LCameron KNikolopoulos DPnevmatikatos D(2022)A data-centric optimization framework for machine learningProceedings of the 36th ACM International Conference on Supercomputing10.1145/3524059.3532364(1-13)Online publication date: 28-Jun-2022
https://dl.acm.org/doi/10.1145/3524059.3532364
Weinbub JKosik R(2022)Computational perspective on recent advances in quantum electronics: from electron quantum optics to nanoelectronic devices and systemsJournal of Physics: Condensed Matter10.1088/1361-648X/ac49c634:16(163001)Online publication date: 22-Feb-2022
https://doi.org/10.1088/1361-648X/ac49c6
Show More Cited By

Index Terms

Optimizing the data movement in quantum transport simulations via data-centric parallel programming
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

Recommendations

High Performance Simulations of Quantum Transport using Manycore Computing
HPCAsia '21 Companion: The International Conference on High Performance Computing in Asia-Pacific Region Companion

The Non-Equilibrium Green’s Function (NEGF) has been widely utilized in the field of nanoscience and nanotechnology to predict carrier transport behaviors in electronic device channels of sizes in a quantum regime. This work explores how much ...
Quantum size effects and transport phenomena in thin Bi layers

For thin Bi films with thicknesses d=10-60nm the dependences of the Hall coefficient, Seebeck coefficient, electrical conductivity, and Hall carrier mobility on d have been obtained at room temperature. Distinct oscillations of the transport properties ...
Application of the R-matrix method in quantum transport simulations

The R-matrix method based on a continuous model is generalized as to become applicable to the atomistic transport simulations with a tight-binding device Hamiltonian. The device elements are introduced as arbitrary atomic clusters in the device area and ...

Comments

Information & Contributors

Information

Published In

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]

Sponsors

SIGHPC: ACM Special Interest Group on High Performance Computing, Special Interest Group on High Performance Computing

In-Cooperation

IEEE CS

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 November 2019

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Qualifiers

Research-article

Funding Sources

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
Horizon 2020 Framework Programme

Conference

SC '19

Sponsor:

SIGHPC

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

November 17 - 19, 2019

Colorado, Denver

Acceptance Rates

Overall Acceptance Rate 1,516 of 6,373 submissions, 24%

Upcoming Conference

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

4
Total Citations
View Citations
414
Total Downloads

Downloads (Last 12 months)23
Downloads (Last 6 weeks)0

Reflects downloads up to 08 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

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
https://doi.org/10.1007/978-981-99-8211-0_10
Rausch OBen-Nun TDryden NIvanov ALi SHoefler TRauchwerger LCameron KNikolopoulos DPnevmatikatos D(2022)A data-centric optimization framework for machine learningProceedings of the 36th ACM International Conference on Supercomputing10.1145/3524059.3532364(1-13)Online publication date: 28-Jun-2022
https://dl.acm.org/doi/10.1145/3524059.3532364
Weinbub JKosik R(2022)Computational perspective on recent advances in quantum electronics: from electron quantum optics to nanoelectronic devices and systemsJournal of Physics: Condensed Matter10.1088/1361-648X/ac49c634:16(163001)Online publication date: 22-Feb-2022
https://doi.org/10.1088/1361-648X/ac49c6
Ziogas ABen-Nun TFernández GSchneider TLuisier MHoefler TTaufer MBalaji PPeña A(2019)A data-centric approach to extreme-scale ab initio dissipative quantum transport simulationsProceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis10.1145/3295500.3357156(1-13)Online publication date: 17-Nov-2019
https://dl.acm.org/doi/10.1145/3295500.3357156

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten