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counted-float

This Python package provides functionality for...

  • counting floating point operations (FLOPs) of numerical algorithms implemented in plain Python, optionally weighted by their relative cost of execution
  • running benchmarks to estimate the relative cost of executing various floating-point operations (requires the benchmarking optional dependency)

Flop weights are computed using a highly curated dataset spanning a wide range of modern CPUs:

  • 21 benchmarks, 16 spec sheets, 12 third party measurements (Agner Fog, uops.info)
  • covering x86 (Intel, AMD) and ARM (Apple, AWS, Azure) architectures
Built-in flop weights, relative to ADD, per architecture

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.

Full documentation: counted-float.readthedocs.io

Installation

Use your favorite package manager such as uv or pip. What you install decides which of the three capabilities you get:

pip install counted-float                  # counting
pip install counted-float[benchmarking]    # + measure this machine's flop costs
pip install counted-float[cli]             # + the counted_float command

Counting is the base install and needs nothing else. Building CountedFloat values, counting contexts, the built-in flop weights, reading benchmark results shipped with the package, and evaluating what counting costs you on your own workload all work here. It is about 17 MB installed.

Benchmarking measures your machine — running the flop benchmark suite to derive weights for the hardware you are on, rather than using the shipped consensus ones. It needs compiled probes (numba) and the packages that describe a CPU (psutil, py-cpuinfo), which is most of the install size: with it, expect roughly 180 MB. Without the extra, calling the benchmark suite tells you what to install instead of failing obscurely, and nothing else is affected.

The CLI adds the counted_float command. The command is always installed; without the extra it reports what to install instead of producing a traceback.

Extras compose, so counted-float[benchmarking,cli] gets you everything.

Quick start

CountedFloat is a drop-in replacement for the built-in float; it is "contagious", so results of math operations involving a CountedFloat stay CountedFloat:

from counted_float import CountedFloat

cf = CountedFloat(1.3)
f = 2.8

result = cf + f  # result = CountedFloat(4.1)

is_float_1 = isinstance(cf, float)  # True
is_float_2 = isinstance(result, float)  # True

FLOPs performed by CountedFloat values are counted while a FlopCountingContext is active:

from counted_float import CountedFloat, FlopCountingContext

cf1 = CountedFloat(1.73)
cf2 = CountedFloat(2.94)

with FlopCountingContext() as ctx:
    _ = cf1 * cf2
    _ = cf1 + cf2

counts = ctx.flop_counts()   # {FlopType.MUL: 1, FlopType.ADD: 1}
counts.total_count()         # 2

Performance overhead

CountedFloat adds counting overhead in two forms — the price of Python-level operator dispatch and result wrapping. How much slower your code runs depends almost entirely on its operation mix:

  • native float ops (+, -, *, /, comparisons): the expensive end of the range — the fixed per-operation dispatch cost dwarfs the nanoseconds of actual arithmetic;
  • patched math.* calls (math.sqrt, math.lgamma, …): a roughly fixed surcharge per call, so the multiple shrinks as the function itself gets more expensive.

counted_float evaluate-overhead measures your own machine — a per-flop-type overhead table, the geomean across types, and a practical mixed workload; see the captured example for representative figures.

Three facts worth knowing:

  • counting state is per-thread: a FlopCountingContext measures only the thread that opened it (open one context per worker thread to measure multi-threaded code, and sum the results). Free-threaded builds (3.14t) are supported and CI-tested;
  • the overhead is inherent and PauseFlopCounting does not reduce it (the instrumented operators still execute; only count registration stops) — the escape hatch for hot uncounted regions is converting back via float(x);
  • overhead never affects count accuracy — counts are exact regardless.

This makes CountedFloat a tool for research and prototyping code, not production hot loops.

numpy counting is an explicit non-goal: np.float64 (float subclass) scalars work and count correctly, but mixing CountedFloat with numpy arrays raises TypeError rather than silently returning uncounted results — see Known limitations for the full boundary.

Documentation

The documentation site covers the rest:

Project links

Data verified by PyPI on Aug 9, 2026
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  • Released:
Newer release available (2.6.0)

1 maintainer

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GitHub Statistics

Data verified by PyPI on Aug 9, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

Apache Software License

Requires

Python >=3.11

Provides Extra

benchmarking cli numba

Classifiers

Report project as malware

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