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Utilizing DDR4 Memory, Intel’s i9 Series, and NVIDIA’s 3000 Line to Analyze the Effect of Overclocking on Performance in Relation to Power Consumption and Temperature

Journal: International Journal of Research in Aeronautical and Mechanical Engineering (Vol.12, No. 2)

Publication Date:

Authors : ; ;

Page : 10-23

Keywords : Overclocking; Central Processor; Graphics Processor; Power Consumption; Computer Performance;

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Abstract

The practice of overclocking, which is the process of increasing the operating frequency of hardware components, is utilized to boost the performance levels of the hardware. This research study focused on assessing the efficiency of overclocking various components by monitoring components such as temperature and power usage. Ultimately, it aimed to determine the optimal settings for overclocking each component individually and evaluate the overall system efficiency when combining these overclocked components. Through utilizing software such as GeekBench 6 and 3D Mark, the study was able to benchmark the performance of components before and after overclocking to test performance. During this, temperatures and power consumption were monitored in order to determine the efficiency of overclocking the system and whether this practice provided any real benefits. With these procedures, it was found that the most efficient overclock in regards to power consumption and temperature was a 22% increase, 11% increase, and 53% increase for the CPU, GPU, and RAM respectively. In summary, this research underscores the significance of overclocking as a means to optimize hardware performance through systematic evaluation of CPU, GPU, and RAM overclocking efficiency. However, while overclocking presents tangible performance benefits, its impact on power consumption and temperature highlights the importance of integrating energy-efficient practices and environmental sustainability considerations into overclocking methodologies, fostering a balance between enhanced performance and responsible resource utilization in computing ecosystems.

Last modified: 2024-02-20 04:59:52