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Python Floating Point Benchmark
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Python Floating Point Benchmark

FPB is a simple tool to measure differentt ways to make computation in Python. The goal is to understand what are the best ways to apply or aggregate data accross many ways.



  • Sum : Sum of a list/array
  • Average : Average of list/array
  • Max : Max of list/array
  • Sinus : Apply sinus function to a list/array
  • Sum 2D : Sum each list/array
  • Correlation : Statistical correlation (not yet)

Tools and libraries

  • Python : Standard libraries such as math or builtins
  • NumPy : Fundamental package for scientific computing with Python
  • Pandas : high-performance, easy-to-use data structures and data analysis tools
  • Dask : Advanced parallelism for analytics, enabling performance at scale
  • CuPy : NumPy-compatible matrix library accelerated by CUDA
  • PyCUDA : Nvidia's CUDA parallel computation API from Python
  • CUDAMat : Performs basic matrix calculations on CUDA-enabled GPUs from Python
  • Numba : Translates a subset of Python and NumPy code into fast machine code
  • MinPy : NumPy interface above MXNet backend (deprecated)
  • SQLite : C-language library that implements a small, fast, self-contained, high-reliability, full-featured, SQL database engine. (for fun)


pip install fpb


The command is pretty simple to use:

usage: fpb [-h] [-i ITERATIONS] [-v] [-j] [-s SIZE] [-S SIZE_Y] [-d DTYPE]
           [-W WARMUP]

Measure Python computation performances

positional arguments:
                        Module to test.

optional arguments:
  -h, --help            show this help message and exit
  -i ITERATIONS, --iterations ITERATIONS
                        Number of iteration to run.
  -v, --verbose         Verbosity level.
  -j, --json            Display output as JSON instead of plain text.
  -s SIZE, --size SIZE  Number of element in X axis.
  -S SIZE_Y, --size_y SIZE_Y
                        Number of element in Y axis, for 2D computation.
  -d {float16,float32,float64,float128}, --dtype {float16,float32,float64,float128}
                        Data type storing elements
  -W WARMUP, --warmup WARMUP
                        Number of iteration to run before start test.

Here's an example of output:

$ fpb sin.numpy -i 3 -s 1000000 -d float16
values         : [14.111995697021484, 14.101982116699219, 14.655590057373047]
memory_errors  : 0
size           : 1000000
test           : fpb.sin.numpy
iterations     : 3
python_version : 3.6.8 (default, Aug 20 2019, 17:12:48) [GCC 8.3.0]
dtype          : float16
numpy_version  : 1.17.2
byte_size      : 2000096
average        : 14.28985595703125
stddev         : 0.258645371196851
percentile_99  : 14.644718170166016
percentile_95  : 14.60123062133789
percentile_90  : 14.546871185302734
percentile_75  : 14.383792877197266
median         : 14.111995697021484
min            : 14.101982116699219
max            : 14.655590057373047
speed          : 71.42422555255513

Design consideration

  • All tests are supposed to be the most efficient way to do in the current framework.
  • Data are prepared before the the test and this operation isn't counted in result.
  • The task timing represents the time to compute and retrieve the result into Python interpreter, not lazy results.
  • Filled memory errors are considered as a normal behavior and counted in result as memory_errors.
  • When sharding is required to dispatch data, we split it with the number of threads available.
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