A GPU-Based Quantum Annealing Simulator for Fully-Connected Ising Models Utilizing Spatial and Temporal Parallelism

A GPU-Based Quantum Annealing Simulator for Fully-Connected Ising Models Utilizing Spatial and Temporal Parallelism

Simulated quantum annealing (SQA) is a probabilistic approximation method to find a solution for a combinatorial optimization problem using digital computers. The processing time of SQA increases exponentially with the number of variables. Therefore, acceleration of SQA is regarded as a very importa...

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Bibliographic Details
Main Authors: Hasitha Muthumala Waidyasooriya, Masanori Hariyama
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Simulated quantum annealing
optimization problems
high performance computing
GPU acceleration
Online Access:https://ieeexplore.ieee.org/document/9057502/
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spelling doaj-9edbda8eb0374374806e8357b9f46d0e2021-03-30T03:12:29ZengIEEEIEEE Access2169-35362020-01-018679296793910.1109/ACCESS.2020.29856999057502A GPU-Based Quantum Annealing Simulator for Fully-Connected Ising Models Utilizing Spatial and Temporal ParallelismHasitha Muthumala Waidyasooriya0https://orcid.org/0000-0001-5108-9891Masanori Hariyama1Graduate School of Information Sciences, Tohoku University, Sendai, JapanGraduate School of Information Sciences, Tohoku University, Sendai, JapanSimulated quantum annealing (SQA) is a probabilistic approximation method to find a solution for a combinatorial optimization problem using digital computers. The processing time of SQA increases exponentially with the number of variables. Therefore, acceleration of SQA is regarded as a very important topic. However, parallel implementation is difficult due to the serial nature of the quantum Monte Carlo algorithm used in SQA. In this paper, we propose a method to implement SQA in parallel on a GPU while preserving the data dependency. According to the experimental results, we have achieved over 97 times speed-up while maintaining the same accuracy-level compared to a single-core CPU implementation.https://ieeexplore.ieee.org/document/9057502/Simulated quantum annealingoptimization problemshigh performance computingGPU acceleration
collection DOAJ
language English
format Article
sources DOAJ
author Hasitha Muthumala Waidyasooriya
Masanori Hariyama
spellingShingle Hasitha Muthumala Waidyasooriya
Masanori Hariyama
A GPU-Based Quantum Annealing Simulator for Fully-Connected Ising Models Utilizing Spatial and Temporal Parallelism
IEEE Access
Simulated quantum annealing
optimization problems
high performance computing
GPU acceleration
author_facet Hasitha Muthumala Waidyasooriya
Masanori Hariyama
author_sort Hasitha Muthumala Waidyasooriya
title A GPU-Based Quantum Annealing Simulator for Fully-Connected Ising Models Utilizing Spatial and Temporal Parallelism
title_short A GPU-Based Quantum Annealing Simulator for Fully-Connected Ising Models Utilizing Spatial and Temporal Parallelism
title_full A GPU-Based Quantum Annealing Simulator for Fully-Connected Ising Models Utilizing Spatial and Temporal Parallelism
title_fullStr A GPU-Based Quantum Annealing Simulator for Fully-Connected Ising Models Utilizing Spatial and Temporal Parallelism
title_full_unstemmed A GPU-Based Quantum Annealing Simulator for Fully-Connected Ising Models Utilizing Spatial and Temporal Parallelism
title_sort gpu-based quantum annealing simulator for fully-connected ising models utilizing spatial and temporal parallelism
publisher IEEE
series IEEE Access
issn 2169-3536
publishDate 2020-01-01
description Simulated quantum annealing (SQA) is a probabilistic approximation method to find a solution for a combinatorial optimization problem using digital computers. The processing time of SQA increases exponentially with the number of variables. Therefore, acceleration of SQA is regarded as a very important topic. However, parallel implementation is difficult due to the serial nature of the quantum Monte Carlo algorithm used in SQA. In this paper, we propose a method to implement SQA in parallel on a GPU while preserving the data dependency. According to the experimental results, we have achieved over 97 times speed-up while maintaining the same accuracy-level compared to a single-core CPU implementation.
topic Simulated quantum annealing
optimization problems
high performance computing
GPU acceleration
url https://ieeexplore.ieee.org/document/9057502/
work_keys_str_mv AT hasithamuthumalawaidyasooriya agpubasedquantumannealingsimulatorforfullyconnectedisingmodelsutilizingspatialandtemporalparallelism
AT masanorihariyama agpubasedquantumannealingsimulatorforfullyconnectedisingmodelsutilizingspatialandtemporalparallelism
AT hasithamuthumalawaidyasooriya gpubasedquantumannealingsimulatorforfullyconnectedisingmodelsutilizingspatialandtemporalparallelism
AT masanorihariyama gpubasedquantumannealingsimulatorforfullyconnectedisingmodelsutilizingspatialandtemporalparallelism
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