MiniApp for Density Matrix Renormalization Group Hamiltonian Application Kernel

Wael Elwasif; Ed D'Azevedo; Arghya Chatterjee; Gonzalo Alvarez; Oscar Hernandez; Vivek Sarkar

doi:10.1109/CLUSTER.2018.00075

MiniApp for Density Matrix Renormalization Group Hamiltonian Application Kernel

Wael Elwasif
, Ed D'Azevedo
, Arghya Chatterjee
, Gonzalo Alvarez
, Oscar Hernandez
, Vivek Sarkar

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

We present two miniapps that implement the core computational kernel of the DMRG++ application, a generic C++ code that implements the Density Matrix Renormalization Group (DMRG) algorithm. The DMRG++ core Kronecker multiplication kernel is formulated using a batched BLAS approach, with implementation that targets both multi-core CPUs using OpenMP and GPGPU using the MAGMA library. The kernel evaluates the matrix-vector multiplication of the target Hamiltonian matrix used in Lanczos algorithm for computing the lowest eigenvalue and eigenvector. The Hamiltonian matrix is expressed compactly as sums of Kronecker products of small dense matrices. We demonstrate improved performance of the miniapp on synthetic problem, and show the performance of the DMRG++ application using a plugin based on the miniapp. We also present an OpenMP miniapp that explores the use of nested parallel constructs to implement the Kronecker multiplication kernel, exploring the use of nested OpenMP worksharing and tasking abstractions to implement the multi-level parallel multiplication algorithm. The miniapp has been used as a co-design vehicle for evaluating features in the OpenMP-4.5 and upcoming OpenMP-5.0 standards.

Original language	English
Title of host publication	Proceedings - 2018 IEEE International Conference on Cluster Computing, CLUSTER 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	590-597
Number of pages	8
ISBN (Electronic)	9781538683194
DOIs	https://doi.org/10.1109/CLUSTER.2018.00075
State	Published - Oct 29 2018
Event	2018 IEEE International Conference on Cluster Computing, CLUSTER 2018 - Belfast, United Kingdom Duration: Sep 10 2018 → Sep 13 2018

Publication series

Name	Proceedings - IEEE International Conference on Cluster Computing, ICCC
Volume	2018-September
ISSN (Print)	1552-5244

Conference

Conference	2018 IEEE International Conference on Cluster Computing, CLUSTER 2018
Country/Territory	United Kingdom
City	Belfast
Period	09/10/18 → 09/13/18

Funding

This work has been supported by the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory (ORNL) is managed by UT-Battelle, LLC for the U. S. Department of Energy under Contract No. DE-AC05-00OR22725. This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

Keywords

Batched BLAS
DMRG
GPGPU
Nested-parallelism
OpenMP

Access to Document

10.1109/CLUSTER.2018.00075

Cite this

Elwasif, W., D'Azevedo, E., Chatterjee, A., Alvarez, G., Hernandez, O., & Sarkar, V. (2018). MiniApp for Density Matrix Renormalization Group Hamiltonian Application Kernel. In Proceedings - 2018 IEEE International Conference on Cluster Computing, CLUSTER 2018 (pp. 590-597). Article 8514919 (Proceedings - IEEE International Conference on Cluster Computing, ICCC; Vol. 2018-September). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CLUSTER.2018.00075

Elwasif, Wael ; D'Azevedo, Ed ; Chatterjee, Arghya et al. / MiniApp for Density Matrix Renormalization Group Hamiltonian Application Kernel. Proceedings - 2018 IEEE International Conference on Cluster Computing, CLUSTER 2018. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 590-597 (Proceedings - IEEE International Conference on Cluster Computing, ICCC).

@inproceedings{c15deb8fc17c4f579ba9b1554fd3e306,

title = "MiniApp for Density Matrix Renormalization Group Hamiltonian Application Kernel",

abstract = "We present two miniapps that implement the core computational kernel of the DMRG++ application, a generic C++ code that implements the Density Matrix Renormalization Group (DMRG) algorithm. The DMRG++ core Kronecker multiplication kernel is formulated using a batched BLAS approach, with implementation that targets both multi-core CPUs using OpenMP and GPGPU using the MAGMA library. The kernel evaluates the matrix-vector multiplication of the target Hamiltonian matrix used in Lanczos algorithm for computing the lowest eigenvalue and eigenvector. The Hamiltonian matrix is expressed compactly as sums of Kronecker products of small dense matrices. We demonstrate improved performance of the miniapp on synthetic problem, and show the performance of the DMRG++ application using a plugin based on the miniapp. We also present an OpenMP miniapp that explores the use of nested parallel constructs to implement the Kronecker multiplication kernel, exploring the use of nested OpenMP worksharing and tasking abstractions to implement the multi-level parallel multiplication algorithm. The miniapp has been used as a co-design vehicle for evaluating features in the OpenMP-4.5 and upcoming OpenMP-5.0 standards.",

keywords = "Batched BLAS, DMRG, GPGPU, Nested-parallelism, OpenMP",

author = "Wael Elwasif and Ed D'Azevedo and Arghya Chatterjee and Gonzalo Alvarez and Oscar Hernandez and Vivek Sarkar",

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Elwasif, W, D'Azevedo, E, Chatterjee, A, Alvarez, G , Hernandez, O & Sarkar, V 2018, MiniApp for Density Matrix Renormalization Group Hamiltonian Application Kernel. in Proceedings - 2018 IEEE International Conference on Cluster Computing, CLUSTER 2018., 8514919, Proceedings - IEEE International Conference on Cluster Computing, ICCC, vol. 2018-September, Institute of Electrical and Electronics Engineers Inc., pp. 590-597, 2018 IEEE International Conference on Cluster Computing, CLUSTER 2018, Belfast, United Kingdom, 09/10/18. https://doi.org/10.1109/CLUSTER.2018.00075

MiniApp for Density Matrix Renormalization Group Hamiltonian Application Kernel. / Elwasif, Wael; D'Azevedo, Ed; Chatterjee, Arghya et al.
Proceedings - 2018 IEEE International Conference on Cluster Computing, CLUSTER 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 590-597 8514919 (Proceedings - IEEE International Conference on Cluster Computing, ICCC; Vol. 2018-September).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Elwasif, Wael

AU - D'Azevedo, Ed

AU - Chatterjee, Arghya

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AU - Hernandez, Oscar

AU - Sarkar, Vivek

PY - 2018/10/29

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N2 - We present two miniapps that implement the core computational kernel of the DMRG++ application, a generic C++ code that implements the Density Matrix Renormalization Group (DMRG) algorithm. The DMRG++ core Kronecker multiplication kernel is formulated using a batched BLAS approach, with implementation that targets both multi-core CPUs using OpenMP and GPGPU using the MAGMA library. The kernel evaluates the matrix-vector multiplication of the target Hamiltonian matrix used in Lanczos algorithm for computing the lowest eigenvalue and eigenvector. The Hamiltonian matrix is expressed compactly as sums of Kronecker products of small dense matrices. We demonstrate improved performance of the miniapp on synthetic problem, and show the performance of the DMRG++ application using a plugin based on the miniapp. We also present an OpenMP miniapp that explores the use of nested parallel constructs to implement the Kronecker multiplication kernel, exploring the use of nested OpenMP worksharing and tasking abstractions to implement the multi-level parallel multiplication algorithm. The miniapp has been used as a co-design vehicle for evaluating features in the OpenMP-4.5 and upcoming OpenMP-5.0 standards.

AB - We present two miniapps that implement the core computational kernel of the DMRG++ application, a generic C++ code that implements the Density Matrix Renormalization Group (DMRG) algorithm. The DMRG++ core Kronecker multiplication kernel is formulated using a batched BLAS approach, with implementation that targets both multi-core CPUs using OpenMP and GPGPU using the MAGMA library. The kernel evaluates the matrix-vector multiplication of the target Hamiltonian matrix used in Lanczos algorithm for computing the lowest eigenvalue and eigenvector. The Hamiltonian matrix is expressed compactly as sums of Kronecker products of small dense matrices. We demonstrate improved performance of the miniapp on synthetic problem, and show the performance of the DMRG++ application using a plugin based on the miniapp. We also present an OpenMP miniapp that explores the use of nested parallel constructs to implement the Kronecker multiplication kernel, exploring the use of nested OpenMP worksharing and tasking abstractions to implement the multi-level parallel multiplication algorithm. The miniapp has been used as a co-design vehicle for evaluating features in the OpenMP-4.5 and upcoming OpenMP-5.0 standards.

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Elwasif W, D'Azevedo E, Chatterjee A, Alvarez G , Hernandez O, Sarkar V. MiniApp for Density Matrix Renormalization Group Hamiltonian Application Kernel. In Proceedings - 2018 IEEE International Conference on Cluster Computing, CLUSTER 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 590-597. 8514919. (Proceedings - IEEE International Conference on Cluster Computing, ICCC). doi: 10.1109/CLUSTER.2018.00075

MiniApp for Density Matrix Renormalization Group Hamiltonian Application Kernel

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