[SciPy-user] Benchmark data

Arnd Baecker arnd.baecker at web.de
Fri Dec 9 14:39:27 CST 2005


On Fri, 9 Dec 2005, Gerard Vermeulen wrote:

> On Fri, 09 Dec 2005 03:14:49 -0700
> Travis Oliphant <oliphant.travis at ieee.org> wrote:
>
> >
> > I'd like people to try out scipy core in SVN.  I made improvements to the
> > buffered ufunc section of code that I think will make a big difference
> > in the recently published benchmarks.

After everything is working fine again on the 64 Bit Opteron (with gcc),
here some results (in total, scipy.base always wins):


python bench.py 4
Python 2.4.2 (#1, Oct  4 2005, 10:10:47)
[GCC 3.4.4]
Optimization flags: -DNDEBUG -g -O3 -Wall -Wstrict-prototypes
CPU info: getNCPUs=2 has_3dnow has_3dnowext has_mmx has_sse has_sse2
is_64bit is_AMD is_Opteron
Numeric-24.0b2
numarray-1.5.0
scipy-core-0.8.1.1617
benchmark size = 4  (vectors of length 256)
label            Numeric       numarray     scipy.base
    1          2.408e-05      0.0004282      2.789e-05
    2          3.409e-05      0.0002079      4.792e-05
    3          1.383e-05      9.108e-05      2.313e-05
    4          3.695e-05      0.0003538      4.506e-05
    5          1.407e-05       0.000108      2.193e-05
    6          1.597e-05      1.311e-05      1.788e-05
    7          1.907e-05      2.789e-05      2.098e-05
    8          1.097e-05      1.812e-05      8.106e-06
    9          0.0001211       0.000653       0.000139
   10           9.68e-05       0.000114      0.0001171
   11          7.105e-05      0.0001619      9.203e-05
TOTAL           0.000458       0.002177       0.000561

> python bench.py 6
Python 2.4.2 (#1, Oct  4 2005, 10:10:47)
[GCC 3.4.4]
Optimization flags: -DNDEBUG -g -O3 -Wall -Wstrict-prototypes
CPU info: getNCPUs=2 has_3dnow has_3dnowext has_mmx has_sse has_sse2
is_64bit is_AMD is_Opteron
Numeric-24.0b2
numarray-1.5.0
scipy-core-0.8.1.1617
benchmark size = 6  (vectors of length 4096)
label            Numeric       numarray     scipy.base
    1          7.892e-05      0.0004399      5.913e-05
    2          0.0001779       0.000349      0.0001638
    3          0.0001261      0.0002031      0.0001411
    4           0.000124      0.0004549      8.583e-05
    5          0.0001349      0.0002279      0.0001361
    6           0.000129      0.0001242      0.0001359
    7          9.513e-05      9.084e-05      6.413e-05
    8          8.297e-05      0.0001061      1.597e-05
    9           0.001184       0.001628       0.001099
   10           0.001072       0.001045       0.001109
   11            0.00086      0.0008881      0.0009079
TOTAL           0.004065       0.005557       0.003918

> python bench.py 11
Python 2.4.2 (#1, Oct  4 2005, 10:10:47)
[GCC 3.4.4]
Optimization flags: -DNDEBUG -g -O3 -Wall -Wstrict-prototypes
CPU info: getNCPUs=2 has_3dnow has_3dnowext has_mmx has_sse has_sse2
is_64bit is_AMD is_Opteron
Numeric-24.0b2
numarray-1.5.0
scipy-core-0.8.1.1617
benchmark size = 11  (vectors of length 4194304)
label            Numeric       numarray     scipy.base
    1            0.07311        0.03584        0.06633
    2             0.1624          0.149         0.1566
    3             0.1356         0.1268         0.1347
    4               0.23          0.118         0.1195
    5             0.1623         0.1485         0.1509
    6              0.128         0.1268         0.1346
    7             0.2279         0.1499           0.12
    8             0.1707         0.1295         0.1293
    9              1.896          1.803          1.664
   10              1.787          2.018          1.666
   11              1.423          1.562          1.321
TOTAL              6.397          6.367          5.663


> python bench.py 12
Python 2.4.2 (#1, Oct  4 2005, 10:10:47)
[GCC 3.4.4]
Optimization flags: -DNDEBUG -g -O3 -Wall -Wstrict-prototypes
CPU info: getNCPUs=2 has_3dnow has_3dnowext has_mmx has_sse has_sse2
is_64bit is_AMD is_Opteron
Numeric-24.0b2
numarray-1.5.0
scipy-core-0.8.1.1617
benchmark size = 12  (vectors of length 16777216)
label            Numeric       numarray     scipy.base
    1             0.2908         0.1507         0.2725
    2             0.6984         0.6152          0.653
    3             0.5147          0.514         0.5547
    4             0.9725         0.4665         0.4707
    5             0.6728         0.6113         0.6202
    6             0.5188         0.5208          0.548
    7             0.9556         0.6282         0.4879
    8             0.6531         0.5119         0.5129
    9              7.725          7.334           6.63
   10              7.553          8.046          6.591
   11              5.931          6.236          5.262
TOTAL              26.49          25.63           22.6

Best, Arnd



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