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With the rapid advancement of science and information technology, Graphics Processing Units (GPUs) have become indispensable tools in contemporary scientific research and industrial production. This paper delves into optimizing GPU performance and energy efficiency, with a specific focus on five aspects: core count and layout, memory hierarchy and cache design, clock speed, specialized hardware units for specific tasks, and interconnections among GPU components. Increasing core count generally enhances GPU performance but also elevates energy consumption. Optimizing core count and layout strikes a balance between performance and energy use. Memory hierarchy and cache design are crucial for handling the inherent parallelism of GPU architecture; optimizing these aspects boosts GPU efficiency and sustainability. Clock speed is a vital performance indicator, with the right speed achieving an optimal balance between performance and energy use. Tailoring hardware units for specific tasks enhances computational efficiency and lowers energy consumption. Optimizing interconnections between GPU components enhances data transfer efficiency, yielding higher performance. Through comprehensive research and optimization in these five areas, this paper introduces innovative strategies and techniques to boost GPU performance and reduce energy consumption, laying the foundation for sustainable computing development.
He Shen (Thu,) studied this question.