04-25-2025, 09:39 AM
Introduction to Triton Kernel Development 2025
![[Image: th_cDJorrWrOfJLVRKHkptnx8oAmGkva6fG.avif]](https://sanet.pics/storage-11/0425/avif/th_cDJorrWrOfJLVRKHkptnx8oAmGkva6fG.avif)
Published 4/2025
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 34m | Size: 157 MB
Master GPU Acceleration with Custom Triton Kernels: From Basics to High-Performance Fused Softmax Implementation Pytorch
What you'll learn
Triton Kernel Development for Nvidia GPUs
Advanced AI Kernel Development
How to write high performance numerical optimizations for PyTorch
Basics of Kernel and Compiler optimziation
Requirements
Experience in machine learning and PyTorch.
Description
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!
Who this course is for
Machine learning developers who wish to author their own kernels.
Homepage:
Screenshots
![[Image: th_WRgQLmpa0A8Z5eCZIizFoIDJpo7ZVMaH.avif]](https://sanet.pics/storage-11/0425/avif/th_WRgQLmpa0A8Z5eCZIizFoIDJpo7ZVMaH.avif)
Download link
rapidgator.net:
nitroflare.com:
Published 4/2025
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 34m | Size: 157 MB
Master GPU Acceleration with Custom Triton Kernels: From Basics to High-Performance Fused Softmax Implementation Pytorch
What you'll learn
Triton Kernel Development for Nvidia GPUs
Advanced AI Kernel Development
How to write high performance numerical optimizations for PyTorch
Basics of Kernel and Compiler optimziation
Requirements
Experience in machine learning and PyTorch.
Description
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!
Who this course is for
Machine learning developers who wish to author their own kernels.
Homepage:
Screenshots
Download link
rapidgator.net:
nitroflare.com:
