Profiling

Performance Engineering: A Practical Starting Map This is the first note in a performance-engineering sequence. It is not a summary of an SRE handbook, a cloud-observability guide, or a treatise on dashboard design. The goal is practical skill at the level of a single machine: understand why a program is slow, decide what to change, and verify that the change worked. The contexts that matter here are things like a chess engine, the microbenchmark problems on HFT University, a hot loop in the Linux kernel, or any single-node program where you own the source and the hardware. The operating system is the boundary we press against; the hardware is the ground truth. ...

CUDA Profiling and Performance Measurement The notes up to this point covered what makes CUDA kernels fast in theory: coalescing, tiling, occupancy, and asynchronous execution. This note covers how to measure whether a kernel is actually fast and how to find the bottleneck when it is not. Profiling is what separates guessing from optimization. Without it, you can apply every best practice and still be slower than a simpler implementation. ...