CS Events

PhD Defense Hybrid CPU-GPU Architectures for Processing Large-Scale Data on Limited Hardware
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Speaker: Azita Nouri

Location : CoRE 301

Committee: 

Prof. Badri Nath (Chair)

Prof. Srinivas Narayana

Prof. Zheng Zhang

Prof. Kazem Cheshmi (External)

Event Type: PhD Defense

Abstract: In the dynamic field of data processing, efficiently managing large-scale data, both offline and in real-time, is a growing challenge. With the limitations of hardware as a focal concern, this dissertation introduces hybrid CPU-GPU frameworks. These are designed specifically to meet the computational needs of data-intensive environments in real time. A central feature of these designs is a unique shared-memory-space approach, which is effective in facilitating data transfers and ensuring synchronization across multiple computations. The research highlights the increasing trend towards swift processing of large-scale data. In sectors like distributed fiber optic sensing, there's a consistent demand for immediate real-time data processing. These designs combine the advantages of both CPU and GPU components, effectively handling fluctuating workloads and addressing computational challenges. Designed for optimal performance in diverse computing environments with limited hardware, the system architecture offers scalability, adaptability, and increased efficiency. Key components of the design, such as shared memory space utilization, process replication, CPU-GPU synchronization, and real-time visualization capabilities, are thoroughly analyzed to demonstrate its capability in real-time data processing.

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Contact  Professor Badri Nath (Chair)