How to Beat PyTorch? Writing a Fast MatMul Kernel in Triton - Tensor Cores, L2 Caching & Auto-Tuning скачать в хорошем качестве

How to Beat PyTorch? Writing a Fast MatMul Kernel in Triton - Tensor Cores, L2 Caching & Auto-Tuning

Matrix Multiplication is the heart of every Transformer model. If it's slow, your model is slow. In this episode of Bielik Anatomy, we build a custom MatMul kernel in OpenAI Triton from scratch. Watch as we take a basic implementation and aggressively optimize it—using memory tricks and hardware acceleration—to match PyTorch’s performance on the GPU. Key concepts covered: Writing a basic kernel with tiling and masking Grouped Block Ordering to maximize L2 Cache hits Tensor Cores: Switching to FP16 for massive speedups Auto-Tuning & Pipelining for final polish Timestamps: 0:00 - Why MatMul matters 1:10 - Basic Kernel Implementation 3:25 - Optimization 1: L2 Cache & Grouping 5:03 - Optimization 2: Auto-Tuning 5:49 - Optimization 3: Tensor Cores 6:49 - Optimization 4: Pipeline & Warps 7:25 - Final Benchmark: Matching PyTorch Code & Resources: GitHub Repo: https://github.com/qooba/bielik-anato... Previous Episode:    • Bielik LM in Triton - Can I Actually Pull ...   #DeepLearning #OpenAITriton #CUDA #BielikAnatomy #GPUOptimization #Bielik