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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 numba optional dependency for achieving accurate results)

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.

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

  • 19 benchmarks, 16 spec sheets, 12 third party measurements (Agner Fog, uops.info)
  • covering x86 (Intel, AMD) and ARM (Apple, AWS, Azure) architectures

Full documentation: counted-float.readthedocs.io

Installation

Use your favorite package manager such as uv or pip:

pip install counted-float           # install without optional dependencies
pip install counted-float[numba]    # install with numba optional dependency
pip install counted-float[cli]      # install with CLI support (click)

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

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. Measured on an Apple M3 Max (measure your own machine with counted_float benchmark-counted-float):

  • native float ops (+, -, *, /, comparisons): roughly 20–40× slower than plain float per operation, environment-dependent (~23× on the M3 Max bisection benchmark);
  • patched math.* calls (math.sqrt, math.exp, …): a roughly fixed ~0.1 µs of overhead per call — about 6–7× for cheap functions like sqrt, and a smaller multiple for costlier ones (the fixed overhead is a smaller share of a slower call).

Two facts worth knowing:

  • 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.

Documentation

The documentation site covers the rest:

Project links

Data verified by PyPI on Jul 15, 2026
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Key dates

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  • Released:
Newer release available (2.6.0)

1 maintainer

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Avatar for bert_pluymers from gravatar.com bert_pluymers

GitHub Statistics

Data verified by PyPI on Jul 15, 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

cli numba

Classifiers

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