ExaTN
Abstract
A challenging scientific problem that will benefit from exascale computing is predictive simulation of two and threedimensional quantum manybody Hamiltonians in condensed matter physics, together with the related strong entanglement problems in quantum chemistry and quantum computing. Efficient numerical simulations will require new insights into wavefunction compression algorithms that scale on extremely heterogeneous HPC architectures. To address this need, we propose to develop a scalable math library of parallel multilinear algebraic numerical primitives for hierarchical tensor compression. Our library will subsequently be used for building an efficient, performanceportable HPC framework for simulating strongly correlated quantum systems. In particular, our will will be the first to deliver a massively parallel implementation of the multiscale entanglement renormalization ansatz (MERA), a hierarchical tensor decomposition quantum scheme capable of expressing local expectation values of strongly entangled higherdimensional quantum systems efficiently. To address the extreme complexity and high computational cost of such schemes, we will automate their parallel implementation by delivering an integrated software framework, ExaTN (Exascale Tensor Networks), that will process hierarchical tensor representations on exascale HPC systems via an asymchronous taskbased programming model. As a specific application, our framework will provide scientists with a capability of simulating strongly entangled quantum systems, thus positioningmore »
 Authors:

 Oak Ridge National Laboratory
 Publication Date:
 Research Org.:
 Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
 Sponsoring Org.:
 USDOE
 OSTI Identifier:
 1546761
 Report Number(s):
 ExaTN; 005866WKSTN00
 DOE Contract Number:
 AC0500OR22725
 Resource Type:
 Software
 Software Revision:
 00
 Software Package Number:
 005866
 Software CPU:
 WKSTN
 Open Source:
 Yes
 Source Code Available:
 Yes
 Country of Publication:
 United States
Citation Formats
McCaskey, Alexander J, Alvarez, Gonzalo, Liakh, Dmytro, Dumitrescu, Eugene, and Mintz, Tiffany. ExaTN.
Computer software. https://www.osti.gov//servlets/purl/1546761. Vers. 00. USDOE. 1 Oct. 2018.
Web.
McCaskey, Alexander J, Alvarez, Gonzalo, Liakh, Dmytro, Dumitrescu, Eugene, & Mintz, Tiffany. (2018, October 1). ExaTN (Version 00) [Computer software]. https://www.osti.gov//servlets/purl/1546761.
McCaskey, Alexander J, Alvarez, Gonzalo, Liakh, Dmytro, Dumitrescu, Eugene, and Mintz, Tiffany. ExaTN.
Computer software. Version 00. October 1, 2018. https://www.osti.gov//servlets/purl/1546761.
@misc{osti_1546761,
title = {ExaTN, Version 00},
author = {McCaskey, Alexander J and Alvarez, Gonzalo and Liakh, Dmytro and Dumitrescu, Eugene and Mintz, Tiffany},
abstractNote = {A challenging scientific problem that will benefit from exascale computing is predictive simulation of two and threedimensional quantum manybody Hamiltonians in condensed matter physics, together with the related strong entanglement problems in quantum chemistry and quantum computing. Efficient numerical simulations will require new insights into wavefunction compression algorithms that scale on extremely heterogeneous HPC architectures. To address this need, we propose to develop a scalable math library of parallel multilinear algebraic numerical primitives for hierarchical tensor compression. Our library will subsequently be used for building an efficient, performanceportable HPC framework for simulating strongly correlated quantum systems. In particular, our will will be the first to deliver a massively parallel implementation of the multiscale entanglement renormalization ansatz (MERA), a hierarchical tensor decomposition quantum scheme capable of expressing local expectation values of strongly entangled higherdimensional quantum systems efficiently. To address the extreme complexity and high computational cost of such schemes, we will automate their parallel implementation by delivering an integrated software framework, ExaTN (Exascale Tensor Networks), that will process hierarchical tensor representations on exascale HPC systems via an asymchronous taskbased programming model. As a specific application, our framework will provide scientists with a capability of simulating strongly entangled quantum systems, thus positioning ORNL as a strategic leader in modeling of strongly correlated materials.},
url = {https://www.osti.gov//servlets/purl/1546761},
doi = {},
year = {2018},
month = {10},
note =
}