Leveraging Emerging Technologies towards Energy-Efficient and High-Performance Computing
| dc.contributor.advisor | Fu, Xin | |
| dc.contributor.committeeMember | Chen, Jinghong | |
| dc.contributor.committeeMember | Pan, Miao | |
| dc.contributor.committeeMember | Mayerich, David | |
| dc.contributor.committeeMember | Wu, Xuqing | |
| dc.creator | Liu, Yiding | |
| dc.creator.orcid | 0000-0002-1520-1441 | |
| dc.date.accessioned | 2023-07-19T22:41:00Z | |
| dc.date.available | 2023-07-19T22:41:00Z | |
| dc.date.created | May 2023 | |
| dc.date.issued | 2023-05-08 | |
| dc.date.updated | 2023-07-19T22:41:01Z | |
| dc.description.abstract | Emerging technologies are revolutionizing the field of high-performance computing (HPC), enabling new levels of speed, energy efficiency, and scalability. This dissertation explores and leverages the potential of emerging technologies, including in-memory acceleration, and quantum computing, to drive energy-efficient and high-performance computing. With the exponential growth of data in recent years, traditional computing architectures (e.g., CPUs and GPUs) have been struggling to keep up with the demands of modern applications. This has resulted in the need for new computing paradigms that can deliver the required performance while also being energy-efficient. In-memory acceleration and quantum computing are two popular emerging technologies that show significant promise in addressing these challenges. In-memory acceleration is a memory-centric approach that leverages large amounts of memory to reduce the data transfer time between the processor and the memory, which allows data to be processed directly in memory. This technology has the potential to greatly increase the speed and efficiency of data processing in a wide range of applications, from big data analysis and machine learning to scientific research and financial modeling. Quantum computing, on the other hand, is a fundamentally new paradigm for HPC that takes advantage of the principles of quantum mechanics to perform efficient calculations that would be impossible with classical computers. While still in the early stages of development, quantum computing has already shown promise for applications such as cryptography, chemistry, optimization, and simulation. This dissertation provides several software and hardware designs to leverage these emerging technologies by incorporating their key features with popular applications into high-performance and energy-efficient computing. We discuss the advantages and disadvantages of these technologies, as well as the challenges that need to be addressed for their widespread adoption. We also examine the impact of these emerging technologies on the future of computing and the potential for new breakthroughs. Overall, this dissertation highlights the importance of emerging technologies in driving energy-efficient and high-performance computing and the potential for in memory acceleration and quantum computing to shape the future of computing. | |
| dc.description.department | Electrical and Computer Engineering, Department of | |
| dc.format.digitalOrigin | born digital | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.citation | Portions of this document appear in: Liu, Yiding, Xingyao Zhang, Donglin Zhuang, Xin Fu, and Shuaiwen Song. "DynamAP: Architectural Support for Dynamic Graph Traversal on the Automata Processor." ACM Transactions on Architecture and Code Optimization (TACO) 19, no. 4 (2022): 1-26; and in: Liu, Yiding, Lening Wang, Amer Qouneh, and Xin Fu. "Enabling PIM-based AES encryption for online video streaming." Journal of Systems Architecture 132 (2022): 102734. | |
| dc.identifier.uri | https://hdl.handle.net/10657/15006 | |
| dc.language.iso | eng | |
| dc.rights | The author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. UH Libraries has secured permission to reproduce any and all previously published materials contained in the work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s). | |
| dc.subject | Emerging technologies | |
| dc.subject | In-memory acceleration | |
| dc.subject | Quantum computing | |
| dc.title | Leveraging Emerging Technologies towards Energy-Efficient and High-Performance Computing | |
| dc.type.dcmi | Text | |
| dc.type.genre | Thesis | |
| thesis.degree.college | Cullen College of Engineering | |
| thesis.degree.department | Electrical and Computer Engineering, Department of | |
| thesis.degree.discipline | Computer and Systems Engineering | |
| thesis.degree.grantor | University of Houston | |
| thesis.degree.level | Doctoral | |
| thesis.degree.name | Doctor of Philosophy |
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