A Practically Applicable Performance Prediction Model Based on Capabilities of Texture Mapping Units for Mobile GPUs

The power consumption models of mobile application processors have emerged as key objects of interest following the tremendous growth in mobile device production given that such consumption is an important factor in the graphics performance of mobile technologies. Conventionally, the performance of...

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Veröffentlicht in:IEEE access 2019, Vol.7, p.102975-102984
Hauptverfasser: Yun, Juwon, Lee, Jinyoung, Kim, Cheong Ghil, Lim, Yeongkyu, Nah, Jae-Ho, Kim, Youngsik, Park, Woo-Chan
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Sprache:eng
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Zusammenfassung:The power consumption models of mobile application processors have emerged as key objects of interest following the tremendous growth in mobile device production given that such consumption is an important factor in the graphics performance of mobile technologies. Conventionally, the performance of the graphics processing units (GPUs) depends critically on texture mapping units, which is why the number of such GPU components and texture fill rates value prominently whenever the GPU performance is evaluated. Our previous work has established a model to predict maximum performance based on unified shaders. By extending the work, this paper developed a practically applicable GPU performance prediction model on the basis of texture mapping performance. The effects of increased texture mapping units on unified shader performance and GPU efficiency were examined, and a performance prediction model based on the number of frames per second (FPS) was constructed. For these purposes, a benchmark related to texture mapping units was formulated and the experiments were conducted to determine utilization factors that are relevant to GPU performance and efficiency. The final stage in model construction involved establishing a relationship between the previously investigated utilization factors and relevant resources that are consumed during graphics processing. The experimental results showed that the proposed prediction model produced an average error rate of 5.77%.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2931290