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<strong><span style="font-size:18.0pt;font-family:"Calibri",sans-serif;color:#C8102E">Leveraging Emerging Technologies towards Energy-Efficient and High-Performance Computing</span></strong><o:p></o:p></p>
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<strong><span style="font-size:13.5pt;font-family:"Calibri",sans-serif">Yiding Liu</span></strong><o:p></o:p></p>
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<span style="font-size:10.5pt;font-family:"Arial",sans-serif">April 14, 2023; 2:00 pm - 3:30 pm (CST)<br>
Zoom: <a href="https://urldefense.com/v3/__https:/uh-edu-cougarnet.zoom.us/j/91275322202?pwd=akhuU1hncVFsZWFYUVRWTEZXOTZZQT09__;!!LkSTlj0I!G8zQm0Eg4JEriYDhfoRKSESrLEiypC6FjHbMmT0Ohzrm2adFasDHa-O0NpRXXp9euDEklp-UpXgLDnWnZJ4nAg$"><span style="color:#C8102E">https://uh-edu-cougarnet.zoom.us/j/3959794030</span></a></span><o:p></o:p></p>
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<strong><span style="font-size:10.5pt;font-family:"Arial",sans-serif">Committee Chair:</span></strong><span style="font-size:10.5pt;font-family:"Arial",sans-serif"><br>
Xin Fu, Ph.D.</span><o:p></o:p></p>
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<strong><span style="font-size:10.5pt;font-family:"Arial",sans-serif">Committee Members:</span></strong><span style="font-size:10.5pt;font-family:"Arial",sans-serif"><br>
Jinghong Chen, Ph.D. | Miao Pan, Ph.D. | David Mayerich, Ph.D. | Xuqing Wu, Ph.D.</span><o:p></o:p></p>
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<strong><span style="font-size:12.0pt;font-family:"Arial",sans-serif;color:#C8102E">Abstract</span></strong><o:p></o:p></p>
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<span style="font-size:10.5pt;font-family:"Arial",sans-serif">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.</span><o:p></o:p></p>
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