[CUDA in Practice] SGEMM — Beating cuBLAS: A Deep Dive into Peak-Performance Matrix Multiplication in Pure CUDA C++0. Preface — The Last Stand of Scalar Compute Warning: Extremely dense content ahead, with many diagrams, heavy bit-manipulation, and memory-mapping derivations. Best read on a PC. Target audience: RMay 29 · 27 min readwingedge777.hashnode.dev
[CUDA in Practice] RoPE — Why Kernel Fusion in Hand-Written Operators Matters: Reducing Memory Traffic and Launch OverheadAI compilers are evolving fast. In many cases, torch.compile plus JIT optimization in PyTorch can deliver striking speedups, to the point that people often say, "hand-written operators are no longer n1May 24 · 8 min readwingedge777.hashnode.dev
[CUDA in Practice] Matrix Transpose — From Padding to XOR Swizzle: The Art of Shared Memory Access OptimizationNote: Text translated by AI. Code crafted by human. Matrix transpose is one of the most fundamental operations in deep learning and high-performance computing. The deceptively simple coordinate swap Apr 9 · 33 min readwingedge777.hashnode.dev
A Deep Dive into DeviceQuery: Understanding Your GPU Hardware0. Preface Before writing a single line of high-performance CUDA code, you must know your silicon. deviceQuery is often the first command a developer runs, yet its output is usually ignored. This postApr 5 · 9 min readwingedge777.hashnode.dev
