GPU Programming with CUDA and Python ( cudapython | 14 hours )
- Python 程式設計經驗
- 具備 NumPy 使用經驗（如 ndarrays、ufuncs 等）
觀眾
- 開發者
CUDA (Compute Unified Device Architecture)是由Nvidia创建的平行计算平台和API。
本課程為講師指導的培訓（線上或線下），適合中級開發者，他們希望使用CUDA來構建Python應用程式，這些應用程式可以在NVIDIA GPU上平行運行。
在本研讨会结束后,参与者将能够:
- 使用Numba編譯器加速在NVIDIA GPU上運行的Python應用程式。
- 創建、編譯並啟動自定義的CUDA核心。
- 管理GPU記憶體。
- 將基於CPU的應用程式轉換為GPU加速的應用程式。
课程格式
- 互動式講座與討論。
- 大量的練習與實踐。
- 在即時實驗室環境中進行實作。
课程定制选项
- 如需為本課程定制培訓，請聯繫我們進行安排。
介紹
- 什麼是GPU編程？
- 為什麼要在Python中使用CUDA？
- 關鍵概念：線程、區塊、網格
CUDA功能與架構概述
- GPU與CPU架構的比較
- 理解SIMT（單指令多線程）
- CUDA編程模型
設置開發環境
- 安裝CUDA工具包與驅動程序
- 安裝Python與Numba
- 設置與驗證環境
並行Programming基礎
- 並行執行介紹
- 理解線程與線程層次結構
- 處理warp與同步
使用Numba編譯器
- Numba介紹
- 使用Numba編寫CUDA核心
- 理解@cuda.jit裝飾器
構建自定義CUDA核心
- 編寫與啟動基本核心
- 使用線程進行逐元素操作
- 管理網格與區塊維度
記憶體Management
- GPU記憶體類型（全局、共享、本地、常量）
- 主機與設備之間的記憶體傳輸
- 優化記憶體使用與避免瓶頸
GPU加速的高級主題
- 共享記憶體與同步
- 使用流進行異步執行
- 多GPU編程基礎
將基於CPU的應用程式轉換為GPU
- 分析CPU代碼
- 識別可並行化的部分
- 將邏輯移植到CUDA核心
疑難排解
- 調試CUDA應用程式
- 常見錯誤與解決方法
- 測試與驗證的工具與技巧
總結與下一步
- 關鍵概念回顧
- GPU編程的最佳實踐
- 持續學習的資源
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