{"title":"用于3D应用的图形处理器性能分析","authors":"J. Issa, S. Figueira","doi":"10.1109/ICTEA.2012.6462881","DOIUrl":null,"url":null,"abstract":"The performance analysis of Graphics Processor Unit (GPU) is important for analyzing and fine-tuning current graphics processors as well as for comparing the performance of different technologies. In this paper, we present an analytical model to calculate the total time it takes for a GPU to retire one frame for a given benchmark. The model also estimates the total retirement time for the same frame for different GPU configuration. The model consists of two stages. The first stage entails establishing the measured baseline for a specific frame on a given graphics card, and the second stage entails adjusting the measured baseline and estimating the time it takes to process all draw calls for the same frame on a different GPU. The model considers the impact of pipeline bottlenecks to process a specific frame, estimates the minimum time it takes to process that frame, and re-parameterize the baseline for a different graphics card to calculate new frame retirement times at two different memory frequencies. We propose an analytical estimation method to estimate frame retirement time for a different GPU at higher memory frequencies based on the new adjusted measured baseline.","PeriodicalId":245530,"journal":{"name":"2012 2nd International Conference on Advances in Computational Tools for Engineering Applications (ACTEA)","volume":"46 4","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Graphics Processor performance analysis for 3D applications\",\"authors\":\"J. Issa, S. Figueira\",\"doi\":\"10.1109/ICTEA.2012.6462881\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The performance analysis of Graphics Processor Unit (GPU) is important for analyzing and fine-tuning current graphics processors as well as for comparing the performance of different technologies. In this paper, we present an analytical model to calculate the total time it takes for a GPU to retire one frame for a given benchmark. The model also estimates the total retirement time for the same frame for different GPU configuration. The model consists of two stages. The first stage entails establishing the measured baseline for a specific frame on a given graphics card, and the second stage entails adjusting the measured baseline and estimating the time it takes to process all draw calls for the same frame on a different GPU. The model considers the impact of pipeline bottlenecks to process a specific frame, estimates the minimum time it takes to process that frame, and re-parameterize the baseline for a different graphics card to calculate new frame retirement times at two different memory frequencies. We propose an analytical estimation method to estimate frame retirement time for a different GPU at higher memory frequencies based on the new adjusted measured baseline.\",\"PeriodicalId\":245530,\"journal\":{\"name\":\"2012 2nd International Conference on Advances in Computational Tools for Engineering Applications (ACTEA)\",\"volume\":\"46 4\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 2nd International Conference on Advances in Computational Tools for Engineering Applications (ACTEA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICTEA.2012.6462881\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 2nd International Conference on Advances in Computational Tools for Engineering Applications (ACTEA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICTEA.2012.6462881","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
摘要
图形处理器单元(Graphics Processor Unit, GPU)的性能分析对于分析和优化当前图形处理器以及比较不同技术的性能具有重要意义。在本文中,我们提出了一个分析模型来计算GPU在给定基准测试中退出一帧所需的总时间。该模型还估计了不同GPU配置下同一帧的总退役时间。该模型包括两个阶段。第一阶段需要为给定显卡上的特定帧建立测量基线,第二阶段需要调整测量基线并估计在不同GPU上处理相同帧的所有绘制调用所需的时间。该模型考虑管道瓶颈对处理特定帧的影响,估计处理该帧所需的最小时间,并重新参数化不同显卡的基线,以计算在两个不同内存频率下的新帧退役时间。我们提出了一种基于新调整的测量基线的分析估计方法来估计不同GPU在更高内存频率下的帧退役时间。
Graphics Processor performance analysis for 3D applications
The performance analysis of Graphics Processor Unit (GPU) is important for analyzing and fine-tuning current graphics processors as well as for comparing the performance of different technologies. In this paper, we present an analytical model to calculate the total time it takes for a GPU to retire one frame for a given benchmark. The model also estimates the total retirement time for the same frame for different GPU configuration. The model consists of two stages. The first stage entails establishing the measured baseline for a specific frame on a given graphics card, and the second stage entails adjusting the measured baseline and estimating the time it takes to process all draw calls for the same frame on a different GPU. The model considers the impact of pipeline bottlenecks to process a specific frame, estimates the minimum time it takes to process that frame, and re-parameterize the baseline for a different graphics card to calculate new frame retirement times at two different memory frequencies. We propose an analytical estimation method to estimate frame retirement time for a different GPU at higher memory frequencies based on the new adjusted measured baseline.