2 docs tagged with "decision-guide"

NVIDIA's math libraries are tuned per architecture by engineers with access to the SASS scheduler, the microarchitecture team, and hardware that hasn't shipped yet. As a rule of thumb rather than a measured benchmark — the actual gap varies by shape, precision, and architecture generation — a hand-written GEMM that reaches something like 60% of cuBLAS's throughput on the same shapes, on the same architecture, is realistically a good hand-written GEMM. The remaining gap is instruction scheduling, tile-size search, and register allocation tuned per compute capability by people who do nothing else. Programming Tensor Cores makes this same point about wmma kernels specifically; this page generalizes it to the whole library landscape and gives a rule for when to reach for one, and when not to.

Every page in this folder makes its own case, and none of them tells you which one to actually pick — that decision depends on facts about your project that no single page can know. This page collects those facts into four questions, a decision table built from them, and one default answer for the common case of not having a second target yet.