[SciPy-user] Benchmark data

Rob Managan managan at llnl.gov
Thu Dec 15 12:45:52 CST 2005


Here is benchmark data for Mac OSX 10.3.9

I also see that scipy.base wins overall. The results are surprisingly 
good for the small vector lengths. Enough so that I inverted the 
order that the packages were tested in from Numeric, numarray, scipy 
to scipy, numarray, Numeric.

The results change a lot!! There must be a cache issue with the data 
or something.


python bench.py 3
Python 2.4.1 (#2, Mar 31 2005, 00:05:10)
[GCC 3.3 20030304 (Apple Computer, Inc. build 1666)]
Optimization flags: -DNDEBUG -g -O3 -Wall -Wstrict-prototypes
CPU info: getNCPUs is_32bit is_ppc
Numeric-24.2
numarray-1.5.0
scipy-core-0.8.6.1671
benchmark size = 3  (vectors of length 64)
label            Numeric       numarray     scipy.base
     1           0.000114       0.001903      0.0001171
     2           0.000108       0.003893      5.198e-05
     3          2.193e-05      0.0006509      3.481e-05
     4            7.2e-05       0.002964      5.817e-05
     5          1.812e-05        0.00088      2.193e-05
     6          1.788e-05      2.098e-05      2.193e-05
     7          4.601e-05      8.011e-05      4.411e-05
     8          1.311e-05      4.292e-05      1.097e-05
     9           0.000149       0.002177       0.000205
    10          0.0001209      0.0002561      0.0004699
    11           0.008709      0.0003729       0.000217
TOTAL            0.00939        0.01324       0.001253


python bench.py 5
Python 2.4.1 (#2, Mar 31 2005, 00:05:10)
[GCC 3.3 20030304 (Apple Computer, Inc. build 1666)]
Optimization flags: -DNDEBUG -g -O3 -Wall -Wstrict-prototypes
CPU info: getNCPUs is_32bit is_ppc
Numeric-24.2
numarray-1.5.0
scipy-core-0.8.6.1671
benchmark size = 5  (vectors of length 1024)
label            Numeric       numarray     scipy.base
     1           0.000159       0.002145      0.0001631
     2            0.00016      0.0005598      8.202e-05
     3          4.196e-05       0.000659      6.199e-05
     4           0.008766       0.008109      9.298e-05
     5          4.506e-05      0.0007169      4.101e-05
     6          5.698e-05      4.411e-05      4.601e-05
     7          0.0003481      0.0001349      7.987e-05
     8          0.0002658      0.0003841      0.0001831
     9          0.0006611       0.004028       0.000767
    10           0.000603      0.0005531      0.0005159
    11          0.0003901      0.0008669       0.000401
TOTAL             0.0115         0.0182       0.002434


% python bench.py 7
Python 2.4.1 (#2, Mar 31 2005, 00:05:10)
[GCC 3.3 20030304 (Apple Computer, Inc. build 1666)]
Optimization flags: -DNDEBUG -g -O3 -Wall -Wstrict-prototypes
CPU info: getNCPUs is_32bit is_ppc
Numeric-24.2
numarray-1.5.0
scipy-core-0.8.6.1671
benchmark size = 7  (vectors of length 16384)
label            Numeric       numarray     scipy.base
     1            0.00989       0.001385      0.0003741
     2            0.00103       0.001106      0.0008531
     3          0.0005479      0.0009282        0.00054
     4           0.003236       0.007418       0.002718
     5           0.000699         0.0172      0.0006359
     6           0.000463      0.0006778        0.00056
     7           0.008911        0.00371       0.004615
     8           0.002682       0.002305        0.01123
     9            0.01685        0.06133        0.03925
    10            0.02049        0.01679        0.01465
    11            0.03076        0.01315         0.0128
TOTAL            0.09556          0.126        0.08823


python bench.py 10
Python 2.4.1 (#2, Mar 31 2005, 00:05:10)
[GCC 3.3 20030304 (Apple Computer, Inc. build 1666)]
Optimization flags: -DNDEBUG -g -O3 -Wall -Wstrict-prototypes
CPU info: getNCPUs is_32bit is_ppc
Numeric-24.2
numarray-1.5.0
scipy-core-0.8.6.1671
benchmark size = 10  (vectors of length 1048576)
label            Numeric       numarray     scipy.base
     1            0.07384        0.01642        0.01819
     2            0.03989        0.03953         0.1033
     3            0.02711        0.02703        0.04777
     4             0.1793         0.1063         0.1082
     5             0.0731        0.03541        0.03534
     6            0.07709        0.02951        0.02816
     7             0.2825         0.1881         0.1056
     8            0.09519         0.1824        0.06622
     9              1.242          0.932         0.9789
    10              1.006          1.081         0.9415
    11             0.7909         0.6992         0.7932
TOTAL              3.887          3.337          3.226

AFTER reversing the order of the three packages
python bench.py 3
[GCC 3.3 20030304 (Apple Computer, Inc. build 1666)]
Optimization flags: -DNDEBUG -g -O3 -Wall -Wstrict-prototypes
CPU info: getNCPUs is_32bit is_ppc
Numeric-24.2
numarray-1.5.0
scipy-core-0.8.6.1671
benchmark size = 3  (vectors of length 64)
label         scipy.base       numarray        Numeric
     1           0.000216       0.001491      8.392e-05
     2          6.485e-05       0.004609      9.108e-05
     3          5.698e-05       0.000685      2.003e-05
     4          7.319e-05       0.001654      5.507e-05
     5          2.289e-05      0.0004721      1.693e-05
     6          2.193e-05      1.788e-05      1.788e-05
     7          4.506e-05      7.391e-05      3.409e-05
     8          2.599e-05      4.005e-05      1.097e-05
     9           0.000242       0.002147       0.000128
    10           0.009578      0.0001972      0.0001061
    11           0.000159        0.00072      7.987e-05
TOTAL            0.01051        0.01211       0.000644


python bench.py 5
Python 2.4.1 (#2, Mar 31 2005, 00:05:10)
[GCC 3.3 20030304 (Apple Computer, Inc. build 1666)]
Optimization flags: -DNDEBUG -g -O3 -Wall -Wstrict-prototypes
CPU info: getNCPUs is_32bit is_ppc
Numeric-24.2
numarray-1.5.0
scipy-core-0.8.6.1671
benchmark size = 5  (vectors of length 1024)
label         scipy.base       numarray        Numeric
     1           0.000123       0.001099      0.0001161
     2          8.082e-05      0.0005021       0.000149
     3          0.0001712      0.0005639      4.196e-05
     4           0.003357       0.001518      9.298e-05
     5          4.506e-05      0.0004861      3.815e-05
     6          4.983e-05      3.982e-05      3.886e-05
     7          0.0001111       0.000108      6.795e-05
     8          0.0002811       0.001387      0.0001831
     9          0.0006509        0.01415      0.0004981
    10          0.0005009       0.000551      0.0004358
    11          0.0004001      0.0008461      0.0003531
TOTAL           0.005771        0.02125       0.002015


python bench.py 7
Python 2.4.1 (#2, Mar 31 2005, 00:05:10)
[GCC 3.3 20030304 (Apple Computer, Inc. build 1666)]
Optimization flags: -DNDEBUG -g -O3 -Wall -Wstrict-prototypes
CPU info: getNCPUs is_32bit is_ppc
Numeric-24.2
numarray-1.5.0
scipy-core-0.8.6.1671
benchmark size = 7  (vectors of length 16384)
label         scipy.base       numarray        Numeric
     1           0.000561       0.001411       0.001281
     2            0.01064       0.001446      0.0008008
     3          0.0007398       0.001116      0.0004001
     4           0.005841       0.003638       0.002384
     5          0.0006442       0.001256       0.001408
     6          0.0004599      0.0004351      0.0004339
     7           0.002262       0.002427        0.00267
     8           0.001092       0.001196       0.001114
     9            0.02235        0.01926        0.01542
    10            0.01499        0.01553         0.0199
    11            0.01259        0.01623         0.0115
TOTAL            0.07216        0.06394        0.05731


python bench.py 10
Python 2.4.1 (#2, Mar 31 2005, 00:05:10)
[GCC 3.3 20030304 (Apple Computer, Inc. build 1666)]
Optimization flags: -DNDEBUG -g -O3 -Wall -Wstrict-prototypes
CPU info: getNCPUs is_32bit is_ppc
Numeric-24.2
numarray-1.5.0
scipy-core-0.8.6.1671
benchmark size = 10  (vectors of length 1048576)
label         scipy.base       numarray        Numeric
     1            0.02554        0.01738        0.05895
     2            0.04277        0.04239        0.04094
     3            0.03304        0.02614        0.02599
     4             0.1083         0.1622         0.1374
     5            0.03525        0.03637        0.03725
     6            0.02828        0.02795        0.02666
     7             0.1027         0.1638         0.1325
     8            0.07478        0.06667        0.07561
     9              1.121         0.8961          1.004
    10              1.059         0.9931         0.8719
    11              0.768         0.7031         0.8103
TOTAL              3.399          3.135          3.221

-- 
*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-
Rob Managan               email managan at llnl.gov
LLNL                      phone: 925-423-0903
P.O. Box 808, L-095       FAX:   925-422-3389
Livermore, CA  94551-0808



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