Posts by George Wang
Hands-On with CK-Tile: Develop and Run Optimized GEMM on AMD GPUs
- 15 April 2025
Composable Kernel (CK-Tile) for ROCm is used to build portable high-performance kernels for accelerating computing, e.g. HPC, DL and LLMs for training and inference workloads. CK-Tile APIs consist of vendor optimized kernels like GEMM, BatchGemm, fused-MHA, fused-MoE, SmoothQuant, element-wise kernels and many other kernels. This blog focuses on creating the most commonly used GEMM kernel, incorporating a vendor-optimized kernel pipeline and policies, and covers key CK-Tile concepts for quick learning.
Unlock Peak Performance on AMD GPUs with Triton Kernel Optimizations
- 10 April 2025
Triton is a domain-specific programming language designed to simplify GPU programming for high-performance tasks, particularly in AI applications. It provides an open-source environment that enables users to write high-level Triton code with greater productivity compared to Nvidia CUDA or AMD HIP. The Triton compiler translates Triton code into optimized GPUs instructions, effectively compiling tensor operations into low-level GPU code. It achieves high efficiency through multiple optimizations passes and leverages the underlying architecture of the GPU. To optimize GPU performance, it is important to have a solid understanding of the Triton compiler and the role it plays in kernel performance. In this blog, we will deep dive into the AMD Triton compiler, introduce Triton kernel compilation, and provide insights on how to create an efficient Triton kernel code.
GEMM Kernel Optimization For AMD GPUs
- 06 February 2025
Matrix multiplication underlies critical computational pathways in AI, with General Matrix Multiplication (GEMM) operations serving as performance-critical kernels in neural network architectures. From fully connected layers to convolutions and transformer attention mechanisms, GEMMs consume substantial computational and memory resources in large language models (LLMs). This blog explores GEMM optimization techniques for AMD GPUs, demonstrating methodologies to significantly enhance computational efficiency and performance scaling.