counted-float

counted-float counts floating-point operations (FLOPs) of numerical algorithms implemented in plain Python, optionally weighted by their relative cost of execution, and can run benchmarks to estimate those relative costs on your own hardware.

The target application area is evaluation of research prototypes of numerical algorithms, where (weighted) FLOP counting can be useful for estimating total computational cost in cases where benchmarking a compiled version (C, Rust, ...) is not feasible or desirable.

Installation

Use your favorite package manager such as uv or pip:

pip install counted-float           # install without numba optional dependency
pip install counted-float[numba]    # install with numba optional dependency

Numba is optional due to its relatively large size (40-50MB, including llvmlite), but without it, benchmarks will not be reliable (they will still run, but not in jit-compiled form).

Where to go next

• Counting FLOPs — the CountedFloat class, the counting model, and counting contexts.
• Math patching semantics — how (and when) math.* functions are instrumented.
• FLOP weights — the built-in consensus weights and how to configure your own.
• Benchmarking — estimating flop weights on your own hardware.
• CLI reference — using counted_float as a stand-alone command-line tool.
• Known limitations — what falls outside the counting model.