Scalable and Fast Characteristic Mode Analysis using GPUs

Authors

  • Khulud Alsultan 1)Department of Computer Science Electrical Engineering, University of Missouri-Kansas City, Kansas City, MO 64110, USA 2)Department of Computer Science and Engineering, King Saud University, Riyadh 11451, Saudi Arabia
  • Mohamed Z. M. Hamdalla Department of Computer Science Electrical Engineering, University of Missouri-Kansas City, Kansas City, MO 64110, USA
  • Sumitra Dey Department of Computer Science Electrical Engineering, University of Missouri-Kansas City, Kansas City, MO 64110, USA
  • Praveen Rao University of Missouri-Columbia, Columbia, MO 65211, USA
  • Ahmed M. Hassan Department of Computer Science Electrical Engineering, University of Missouri-Kansas City, Kansas City, MO 64110, USA

DOI:

https://doi.org/10.13052/2022.ACES.J.370203

Keywords:

Big data applications, characteristic mode analysis, fast, graphics processing unit, method of moments, scalability

Abstract

Characteristic mode analysis (CMA) is used in the design and analysis of a wide range of electromagnetic devices such as antennas and nanostructures. The implementation of CMA involves the evaluation of a large method of moments (MoM) complex impedance matrix at every frequency. In this work, we use different open-source software for the GPU acceleration of the CMA. This open-source software comprises a wide range of computer science numerical and machine learning libraries not typically used for electromagnetic applications. Specifically, this paper shows how these different Python-based libraries can optimize the computational time of the matrix operations that compose the CMA algorithm. Based on our computational experiments and optimizations, we propose an approach using a GPU platform that is able to achieve up to 16×× and 26×× speedup for the CMA processing of a single 15k ×× 15k MoM matrix of a perfect electric conductor scatterer and a single 30k ×× 30k MoM matrix of a dielectric scatterer, respectively. In addition to improving the processing speed of CMA, our approach provided the same accuracy as independent CMA simulations. The speedup, efficiency, and accuracy of our CMA implementation will enable the analysis of electromagnetic systems much larger than what was previously possible at a fraction of the computational time.

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Author Biographies

Khulud Alsultan, 1)Department of Computer Science Electrical Engineering, University of Missouri-Kansas City, Kansas City, MO 64110, USA 2)Department of Computer Science and Engineering, King Saud University, Riyadh 11451, Saudi Arabia

Khulud Alsultan received the B.S. degree in computer science from King Saud University, Riyadh, Saudi Arabia in 2006, the M.Sc. degree in computer science from Kent State University, Kent, OH, USA, in 2013, and the Ph.D. degree in computer science and telecommunication and computer networking from the University of Missouri-Kansas City, Kansas City, MO, USA, in 2021.

She is currently a Lecturer with the Department of Computer Science, King Saud University (KSU). Her research interest includes Big Data and data analysis, and its applications in healthcare and electrical engineering. She served as an external reviewer for several conferences, such as BEXA 2019 and BDA 2019. While being an active researcher, she obtained the Preparing Future Faculty program (PFF) graduate certificate at UMKC. Moreover, Dr. Alsultan received awards including the Grace Hopper Celebration Student Scholarship in 2018, and the Graduate Student Travel Grant, UMKC, Kansas City, MO, USA, in 2019.

Mohamed Z. M. Hamdalla, Department of Computer Science Electrical Engineering, University of Missouri-Kansas City, Kansas City, MO 64110, USA

Mohamed Hamdalla received the B.Sc. and M.Sc. degrees from the Arab Academy for Science, Technology and Maritime Transport, Alexandria, Egypt, in 2012 and 2016, respectively, both in electronics and communications engineering, and the Ph.D. degree in electrical engineering from the University of Missouri-Kansas City, Kansas City, MO, USA, in 2021.

His current research interests include antennas, metamaterials, microwave filters, electromagnetic compatibility and interference, characteristic mode theory, and applications.

Sumitra Dey, Department of Computer Science Electrical Engineering, University of Missouri-Kansas City, Kansas City, MO 64110, USA

Sumitra Dey received the B.Tech. degree in radio physics and electronics from the University of Calcutta, Kolkata, India, in 2014, the M.Tech. degree in RF and microwave communication engineering from IIEST Shibpur, West Bengal, India, in 2016, and the Ph.D. degree in electrical engineering from the University of Missouri-Kansas City, Kansas City, MO, USA, in 2021.

She is currently a Lead Product Engineer with Multi-physics System Analysis Group, Cadence Design Systems, San Jose, CA, USA. Her research interests include computational electromagnetics, signal/power integrity in high frequency, nano-electromagnetics, nondestructive evaluation, experimental microwave and terahertz imaging, AI/ML based optimization of electromagnetic response, multilayer Green’s functions, characteristic mode theory, and applications.

Dr. Dey was awarded the Honorable Mention in Student paper competition in 2020 Applied Computational Electromagnetic Society (ACES) Conference, Monterey, CA, USA, Honorable Mention in Student paper competition in 2019 IEEE APS/USNC URSI Symposium, Atlanta, GA, USA, Honorable Mention in 2018 Altair FEKO Student Design Competition, and the Best Student Paper in IEEE CALCON 2015, Kolkata,India.

Praveen Rao, University of Missouri-Columbia, Columbia, MO 65211, USA

Praveen Rao received the M.S. and Ph.D. degrees in computer science from the University of Arizona, Tucson, AZ, USA, in 2007 and 2001, respectively.

He is currently a tenured Associate Professor with joint appointment in the Department of Health Management & Informatics and the Department of Electrical Engineering & Computer Science at the University of Missouri (MU), Columbia, MO, USA.

His research interests are in the areas of Big Data management, data science, health informatics, and cybersecurity. He directs the Scalable Data Science (SDS) Lab at MU. His research, teaching, and outreach activities have been supported by the National Science Foundation (NSF), Air Force Research Lab (AFRL), the National Endowment for the Humanities (NEH), the National Institutes of Health (NIH), the University of Missouri System (Tier 1 grant, Tier 3 grant), University of Missouri Research Board, and companies. He is a Co-PI for the NSF IUCRC Center for Big Learning. At MU, he is a core faculty of the Center for Biomedical Informatics (CBMI), the Cybersecurity Center, the MU Institute for Data Science and Informatics, and the CERI Center. He is a core scientist of the Washington University Center for Diabetes Translation Research funded by NIH. He is a Senior Member of the ACM (2020) andIEEE (2015).

Ahmed M. Hassan, Department of Computer Science Electrical Engineering, University of Missouri-Kansas City, Kansas City, MO 64110, USA

Ahmed M. Hassan received the B.Sc. (with highest honors) and M.Sc. degrees from Cairo University, Giza, Egypt, in 2004 and 2006, respectively, both in electronics and communications engineering. He received the Ph.D. degree in electrical engineering from the University of Arkansas, Fayetteville, AR, USA, in 2010.

From 2011 to 2012, he was a Postdoctoral Researcher with the Department of Electrical Engineering, University of Arkansas. From 2012 to 2015, he was a Postdoctoral Researcher with the National Institute of Standards and Technology, Gaithersburg, MD, USA. He is currently an Assistant Professor with the Computer Science Electrical Engineering Department, University of Missouri-Kansas City. His current research interests include nanoelectromagnetics, bioelectromagnetics, electromagnetic compatibility and interference, nondestructive evaluation, experimental microwave, and terahertz imaging.

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Published

2022-07-09

How to Cite

[1]
K. . Alsultan, M. Z. M. . Hamdalla, S. . Dey, P. . Rao, and A. M. . Hassan, “Scalable and Fast Characteristic Mode Analysis using GPUs”, ACES Journal, vol. 37, no. 02, pp. 156–167, Jul. 2022.

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2022: Vol 37 Iss 2

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Articles