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| 研究生： |
曾豐源 Tseng, Feng-Yuan |
|---|---|
| 論文名稱： |
大數據分析於GPU平台之效能評估：以影像辨識為例 Evaluation of Big Data Analytical Performance on GPU Platforms: Computer Vision as an Example |
| 指導教授： |
胡毓忠
Hu, Yuh-Jong |
| 口試委員: |
黃瀚萱
Huang, Hen-Hsen 陳弘軒 Chen, Hung-Hsuan |
| 學位類別： |
碩士
Master |
| 系所名稱： |
理學院 - 資訊科學系碩士在職專班 Excutive Master Program of Computer Science |
| 論文出版年： | 2021 |
| 畢業學年度： | 109 |
| 語文別： | 中文 |
| 論文頁數： | 32 |
| 中文關鍵詞： | 大數據分析 、深度學習 、ImageNet 、NVIDIA 、GPU 、NVIDIA DGX A100 、NVIDIA DGX Station |
| 外文關鍵詞： | Big Data Analysis, Deep Learning, ImageNet, NVIDIA, GPU, NVIDIA DGX A100, NVIDIA DGX Station |
| DOI URL： | http://doi.org/10.6814/NCCU202101203 |
| 相關次數： | 點閱：101 下載：14 |
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本研究以ImageNet Large Scale Visual Recognition Challenge (ILSVRC)作為資料集，結合ResNet50深度學習模型，從企業角度為出發點，比較不同的GPU運算環境在AI 大數據分析流程中，探討硬體效能及性價比。本研究以政大電算中心私有雲NVIDIA DGX A100、NVIDIA DGX Station，以及Desktop Computer三種GPU運算環境進行效能測試，並且利用系統監控技術，取得各流程中硬體資源的使用情況，並分析總體效能。因此實驗結果顯示，NVIDIA DGX A100在訓練階段能夠減少模型訓練時間，而在上線階段Desktop Computer其性價比優於NVIDIA DGX A100和NVIDIA DGX Station。
This research adopts the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) as data set, combined with the ResNet50 deep learning model to compare the performance and cost-effectiveness of a hardware under different GPU computing environments applied throughout the AI big data analysis process from an enterprise’s perspective. Performance tests are conducted under three different GPU computing environments, including NVIDIA DGX A100 and NVIDIA DGX Station, hosted as two seperate private clouds owned by the NCCU Computer Center, and the typical desktop computer. We use the system monitoring technology to obtain the usage of hardware resources in each analysis process and to examine the overall performance. The results show that NVIDIA DGX A100 can reduce the time needed for model training during training phase, while Desktop Computer is more cost-effective than NVIDIA DGX A100 and NVIDIA DGX Station during the online phase.
誌謝 i
摘要 ii
Abstract iii
目錄 iv
圖目錄 vi
表目錄 vii
1 導論 1
1.1 研究動機 1
1.2 研究目的 2
1.3 論文架構 3
2 研究背景 4
2.1 AI大數據分析流程 4
2.2 ImageNet數據集 6
2.3 ResNet網路模型 6
2.4 政大NVIDIA GPU運算環境架構 7
3 相關研究 11
3.1 模型訓練基準評估研究案例 11
3.2 模型推理基準評估研究案例 12
4 研究構架與方法 14
4.1 實驗資料集 14
4.2 選擇網路模型 15
4.3 各GPU 運算環境之配置 16
4.4 訓練階段實驗方法 16
4.5 上線階段實驗方法 18
5 實驗結果 20
5.1 訓練階段之實驗結果 20
5.2 上線階段之實驗結果 26
5.3 總體效能與性價比分析 27
6 結論與未來展望 30
6.1 結論 30
6.2 未來展望 30
參考文獻 31
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