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所在平台: Udemy |
课程主页: https://www.udemy.com/course/introduction-to-triton-kernel-development/
课程评论：没有评论
课程名称：人工智能开发大师班：AI GPU性能优化 课程概述：本课程将帮助您无需编写CUDA代码，即可释放GPU加速的强大力量！这是一门实践导向的课程，指导您在Google Colab的T4 GPU上使用Triton和PyTorch创建自定义高性能内核，非常适合希望优化深度学习模型的机器学习工程师和研究人员。课程从Triton基础知识开始，逐步构建，最终实现高效的融合softmax内核——这是变换器模型中的关键组件。通过与PyTorch本地实现的详细比较，您将深入了解性能优化原理和实际加速技术。 课程内容涵盖： - Triton编程模型及核心概念 - 现代GPU架构基础知识和内存层次结构 - PyTorch集成技术及性能基准 - PyTorch和Triton中softmax的逐步实现 - 深入探讨Triton编译器及其优化过程 - 最大吞吐量的内存访问模式和分块策略 - 寄存器、共享内存及L1/L2缓存利用技巧 - 性能分析与瓶颈识别 - 真实部署的高级优化策略 - 使用Google Colab T4 GPU的动手实践 您不仅将学习如何编写内核，还会理解使其快速的底层硬件交互。通过将PyTorch的本地操作与我们自定义的Triton实现进行比较，您将培养出优化自己项目中关键代码路径的直觉。课程不要求CUDA经验，仅需具备Python和基本的PyTorch知识。立即加入，以将硬件加速技能添加到您的深度学习工具包中，并将模型性能提升到更高的水平！
Unlock the power of GPU acceleration without writing CUDA code! This hands-on course guides you through creating custom high-performance kernels using Triton and PyTorch on Google Colab's T4 GPUs. Perfect for ML engineers and researchers who want to optimize their deep learning models.You'll start with Triton fundamentals and progressively build toward implementing an efficient fused softmax kernel - a critical component in transformer models. Through detailed comparisons with PyTorch's native implementation, you'll gain insights into performance optimization principles and practical acceleration techniques.This comprehensive course covers:Triton programming model and core conceptsModern GPU architecture fundamentals and memory hierarchyPyTorch integration techniques and performance baselinesStep-by-step implementation of softmax in both PyTorch and TritonDeep dive into the Triton compiler and its optimization passesMemory access patterns and tiling strategies for maximum throughputRegister, shared memory, and L1/L2 cache utilization techniquesPerformance profiling and bottleneck identificationAdvanced optimization strategies for real-world deploymentHands-on practice with Google Colab T4 GPUsYou'll not just learn to write kernels, but understand the underlying hardware interactions that make them fast. By comparing PyTorch's native operations with our custom Triton implementations, you'll develop intuition for when and how to optimize critical code paths in your own projects.No CUDA experience required - just Python and basic PyTorch knowledge. Join now to add hardware acceleration skills to your deep learning toolkit and take your models to the next level of performance!
