Machine learning systems sit at the heart of modern AI workloads. In these systems, performance often comes down to the quality of a small number of GPU kernels. Attention kernels, LLM prefill and decode kernels, low-precision block-scaled GEMMs, fused MoE layers, and other large fused kernels all directly shape end-to-end speed in both training and serving.
To make these kernels fast, however, we need more than a list of optimization tricks. Modern GPUs are no longer simple variations of the same old design. Recent architectures introduce richer memory spaces, new access patterns, and increasingly specialized execution units. To program them well, we need both a clear mental model of the hardware and a practical understanding of how high-performance kernels are built. This book is about developing both.
The book follows a simple progression: first understand the GPU hardware, then learn the programming model we will use, and finally build state-of-the-art kernels step by step. Our main target is the Blackwell generation, and our main running examples are General Matrix-Matrix Multiplication (GEMM) and FlashAttention. Along the way, we will also study the core ingredients behind GPU optimization: data layout, asynchronous data movement, and asynchronous coordination.
