GPU Programming with CUDA and Python ( cudapython | 14 hours )
- Python 程式设计经验
- 具备 NumPy 使用经验（如 ndarrays、ufuncs 等）
观众
- 开发者
CUDA (Compute Unified Device Architecture)是由Nvidia创建的平行计算平台和API。
本课程为讲师指导的培训（线上或线下），适合中级开发者，他们希望使用CUDA来构建Python应用程式，这些应用程式可以在NVIDIA GPU上平行运行。
在本研讨会结束后,参与者将能够:
- 使用Numba编译器加速在NVIDIA GPU上运行的Python应用程式。
- 创建、编译并启动自定义的CUDA核心。
- 管理GPU记忆体。
- 将基于CPU的应用程式转换为GPU加速的应用程式。
课程格式
- 互动式讲座与讨论。
- 大量的练习与实践。
- 在即时实验室环境中进行实作。
课程定制选项
- 如需为本课程定制培训，请联系我们进行安排。
介绍
- 什么是GPU编程？
- 为什么要在Python中使用CUDA？
- 关键概念：线程、区块、网格
CUDA功能与架构概述
- GPU与CPU架构的比较
- 理解SIMT（单指令多线程）
- CUDA编程模型
设置开发环境
- 安装CUDA工具包与驱动程序
- 安装Python与Numba
- 设置与验证环境
并行Programming基础
- 并行执行介绍
- 理解线程与线程层次结构
- 处理warp与同步
使用Numba编译器
- Numba介绍
- 使用Numba编写CUDA核心
- 理解@cuda.jit装饰器
构建自定义CUDA核心
- 编写与启动基本核心
- 使用线程进行逐元素操作
- 管理网格与区块维度
记忆体Management
- GPU记忆体类型（全局、共享、本地、常量）
- 主机与设备之间的记忆体传输
- 优化记忆体使用与避免瓶颈
GPU加速的高级主题
- 共享记忆体与同步
- 使用流进行异步执行
- 多GPU编程基础
将基于CPU的应用程式转换为GPU
- 分析CPU代码
- 识别可并行化的部分
- 将逻辑移植到CUDA核心
疑难排解
- 调试CUDA应用程式
- 常见错误与解决方法
- 测试与验证的工具与技巧
总结与下一步
- 关键概念回顾
- GPU编程的最佳实践
- 持续学习的资源
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