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所在平台: Udemy |
课程主页: https://www.udemy.com/course/mastering-gpu-parallel-programming-with-cuda/
课程评论：没有评论
**课程名称：** Mastering GPU Parallel Programming with CUDA: ( HW & SW ) **课程概述：** 本课程是一门实践性课程，旨在教授学员如何利用现代 GPU 强大的并行处理能力。课程将从 GPU 硬件基础知识入手，追溯从 Fermi 到 Hopper 的历代旗舰架构演进，并通过代码实践实验室，学习如何编写、分析和优化高性能的 CUDA 内核。 **课程亮点：** * **GPU vs. CPU 基础：** 深入理解 GPU 在数据并行工作负载上的优势。 * **架构演进：** 掌握对性能至关重要的硬件设计进展。 * **CUDA 工具包安装：** 学习在 Windows, Linux, WSL 上安装 CUDA 工具包，并进行初步检查。 * **核心 CUDA 概念：** 通过向量加法等实验，理解线程、块、网格以及内存层级结构。 * **性能分析与调优：** 运用 Nsight Compute / nvprof 分析占用率、隐藏延迟、打破瓶颈。 * **二维索引：** 学习编写高效的矩阵内核，用于实际的线性代数任务。 * **优化策略：** 掌握处理非2的幂次方数据、利用共享内存、最大化带宽、最小化 Warp 发散。 * **可靠的调试与错误处理：** 使用运行时 API 检查，编写生产级别的 CUDA 代码。 **学习成果：** 完成课程后，学员将能够设计、分析和精调在当今 GPU 上高效运行的 CUDA 内核，从而胜任复杂的科学计算、工程和人工智能工作负载。 **免责声明：** 本课程为独立培训资源，与 NVIDIA Corporation 无任何关联、赞助或认可。“CUDA”、“Nsight”及架构代号均为 NVIDIA 的商标，仅在此作为事实参考使用。
This hands-on course teaches you how to unlock the huge parallel-processing power of modern GPUs with CUDA. You'll start with the fundamentals of GPU hardware, trace the evolution of flagship architectures (Fermi → Pascal → Volta → Ampere → Hopper), and learn-through code-along labs-how to write, profile, and optimize high-performance kernels.This is an independent training resource. It is not sponsored by, endorsed by, or otherwise affiliated with NVIDIA Corporation. "CUDA", "Nsight", and the architecture codenames are trademarks of NVIDIA and are used here only as factual references.What you'll masterGPU vs. CPU fundamentals - why GPUs dominate data-parallel workloads.Generational design advances - the hardware features that matter most for performance.CUDA toolkit installation - Windows, Linux, and WSL, plus first-run sanity checks.Core CUDA concepts - threads, blocks, grids, and the memory hierarchy, built up with labs such as vector addition.Profiling & tuning with Nsight Compute / nvprof - measure occupancy, hide latency, and break bottlenecks.2-D indexing for matrices - write efficient kernels for real-world linear-algebra tasks.Optimization playbook - handle non-power-of-two data, leverage shared memory, maximize bandwidth, and minimize warp divergence.Robust debugging & error handling - use runtime-API checks to ship production-ready code.By the end, you'll be able to design, analyze, and fine-tune CUDA kernels that run efficiently on today's GPUs-equipping you to tackle demanding scientific, engineering, and AI workloads.
