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Python Floating Point Benchmark
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README.rst

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.

Features

Operations

  • 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)

Install

pip install fpb

Usage

The command is pretty simple to use:

usage: fpb [-h] [-i ITERATIONS] [-v] [-j] [-s SIZE] [-S SIZE_Y] [-d DTYPE]
           [-W WARMUP]
           {sum.python,sum.ctypes,sum.numpy,sum.pandas,sum.dask,sum.cupy,sum.minpy,sum.numba,sum2d.python,sum2d.python_gen,sum2d.numpy,sum2d.pandas,sum2d.dask,sin.python,sin.numpy,sin.pandas,sin.dask,sin.cupy,sin.minpy,sin.numba,avg.python,avg.numpy,avg.pandas,avg.cupy,avg.minpy,avg.numba,max.python,max.numpy,max.pandas,max.dask,max.cupy,max.minpy,max.numba}

Measure Python computation performances

positional arguments:
  {sum.python,sum.ctypes,sum.numpy,sum.pandas,sum.dask,sum.cupy,sum.minpy,sum.numba,sum2d.python,sum2d.python_gen,sum2d.numpy,sum2d.pandas,sum2d.dask,sin.python,sin.numpy,sin.pandas,sin.dask,sin.cupy,sin.minpy,sin.numba,avg.python,avg.numpy,avg.pandas,avg.cupy,avg.minpy,avg.numba,max.python,max.numpy,max.pandas,max.dask,max.cupy,max.minpy,max.numba}
                        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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