diff options
Diffstat (limited to 'benchmarks')
| -rw-r--r-- | benchmarks/benchmarks/bench_lib.py | 42 |
1 files changed, 35 insertions, 7 deletions
diff --git a/benchmarks/benchmarks/bench_lib.py b/benchmarks/benchmarks/bench_lib.py index e6c91a27c..7a6b3f01d 100644 --- a/benchmarks/benchmarks/bench_lib.py +++ b/benchmarks/benchmarks/bench_lib.py @@ -9,20 +9,48 @@ import numpy as np class Pad(Benchmark): - """Benchmarks for `numpy.pad`.""" + """Benchmarks for `numpy.pad`. + + When benchmarking the pad function it is useful to cover scenarios where + the ratio between the size of the input array and the output array differs + significantly (original area vs. padded area). This allows to evaluate for + which scenario a padding algorithm is optimized. Furthermore involving + large range of array sizes ensures that the effects of CPU-bound caching is + visible. + + The table below shows the sizes of the arrays involved in this benchmark: + + +-----------------+----------+-----------+-----------+-----------------+ + | shape | original | padded: 1 | padded: 8 | padded: (0, 32) | + +=================+==========+===========+===========+=================+ + | (2 ** 22,) | 32 MiB | 32.0 MiB | 32.0 MiB | 32.0 MiB | + +-----------------+----------+-----------+-----------+-----------------+ + | (1024, 1024) | 8 MiB | 8.03 MiB | 8.25 MiB | 8.51 MiB | + +-----------------+----------+-----------+-----------+-----------------+ + | (256, 256, 1) | 256 KiB | 786 KiB | 5.08 MiB | 11.6 MiB | + +-----------------+----------+-----------+-----------+-----------------+ + | (4, 4, 4, 4) | 2 KiB | 10.1 KiB | 1.22 MiB | 12.8 MiB | + +-----------------+----------+-----------+-----------+-----------------+ + | (1, 1, 1, 1, 1) | 8 B | 1.90 MiB | 10.8 MiB | 299 MiB | + +-----------------+----------+-----------+-----------+-----------------+ + """ param_names = ["shape", "pad_width", "mode"] params = [ - [(1000,), (10, 100), (10, 10, 10)], - [1, 3, (0, 5)], + # Shape of the input arrays + [(2 ** 22,), (1024, 1024), (256, 128, 1), + (4, 4, 4, 4), (1, 1, 1, 1, 1)], + # Tested pad widths + [1, 8, (0, 32)], + # Tested modes: mean, median, minimum & maximum use the same code path + # reflect & symmetric share a lot of their code path ["constant", "edge", "linear_ramp", "mean", "reflect", "wrap"], ] def setup(self, shape, pad_width, mode): - # avoid np.zeros or np.empty's lazy allocation. - # np.full causes pagefaults to occur during setup - # instead of during the benchmark - self.array = np.full(shape, 0) + # Make sure to fill the array to make the OS page fault + # in the setup phase and not the timed phase + self.array = np.full(shape, fill_value=1, dtype=np.float64) def time_pad(self, shape, pad_width, mode): np.pad(self.array, pad_width, mode) |