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

التفاصيل البيبلوغرافية
العنوان: A Practically Applicable Performance Prediction Model Based on Capabilities of Texture Mapping Units for Mobile GPUs
المؤلفون: Yeongkyu Lim, Woo-Chan Park, Juwon Yun, Jinyoung Lee, Cheong-Ghil Kim, Youngsik Kim, Jae-Ho Nah
المصدر: IEEE Access, Vol 7, Pp 102975-102984 (2019)
بيانات النشر: IEEE, 2019.
سنة النشر: 2019
مصطلحات موضوعية: General Computer Science, Computer science, General Engineering, Frame rate, performance evaluation, Computer graphics, Computer engineering, Unified shader model, Benchmark (computing), Performance prediction, General Materials Science, lcsh:Electrical engineering. Electronics. Nuclear engineering, Graphics, performance analysis, Shader, Texture mapping, dynamic voltage scaling, prediction method, lcsh:TK1-9971, ComputingMethodologies_COMPUTERGRAPHICS
الوصف: 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%.
اللغة: English
تدمد: 2169-3536
URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::97a3368f37cf659a3f28358814861773
https://ieeexplore.ieee.org/document/8777105/
حقوق: OPEN
رقم الأكسشن: edsair.doi.dedup.....97a3368f37cf659a3f28358814861773
قاعدة البيانات: OpenAIRE