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From "Simon Chow" <simon.harm...@gmail.com>
Subject Re: [general][evaluation] I did a performance evaluation using Scimark2
Date Sat, 08 Mar 2008 05:32:11 GMT
I am sorry about that...
In my Gmail browser, they are some pretty good-looking tables
maybe Gmail don't support table well :(
So this is the plain text version ( and a snapshot.jpeg with some content is
in attachment )


Platform:
Intel(R) Xeon(TM) CPU 2.80GHz*4.
Linux localhost 2.6.18-8.el5xen;
Mem:4GB

Harmony (M5)

-Xms1500m -Xmx1500m -Xem:server jnt.scimark2.commandline
Composite Score    FFT(1024)    SOR(100*100)    Monte Carlo    Sparse
matmult(N=1000,nz=5000)    LU(100*100)
193.99    223.91    366.62    28.42    184.19    166.83
194.05    222.20    370.43    28.04    183.16    166.42
193.67    223.05    369.72    28.61    181.29    165.70
193.41    221.29    371.28    27.69    182.04    164.74
194.34    222.48    371.00    28.17    183.32    166.75

-Xms1500m -Xmx1500m -Xem:server jnt.scimark2.commandline -large
Composite Score    FFT(1048576)    SOR(1000*1000)    Monte Carlo    Sparse
matmult(N=100000,nz=1000000)    LU(1000*1000)
179.31    37.93    359.34    27.18    289.51    182.60
178.31    35.84    359.34    28.08    288.78    179.50
179.35    37.19    258.66    28.08    289.43    183.40
179.02    35.63    360.01    27.14    289.92    182.40
179.80    37.44    360.01    27.25    290.08    184.21



Sun java version "1.5.0_12"

-Xms1500m -Xmx1500m -server jnt.scimark2.commandline
Composite Score    FFT(1024)    SOR(100*100)    Monte Carlo    Sparse
matmult(N=1000,nz=5000)    LU(100*100)
427.30    252.57    593.82    22.51    321.41    946.18
431.48    272.11    596.21    22.16    322.68    944.21
432.80    273.99    596.77    22.54    322.20    948.48
428.75    256.96    596.03    22.58    323.63    944.54
432.90    276.25    597.32    22.59    323.16    945.19

-Xms1500m -Xmx1500m –server jnt.scimark2.commandline -large
Composite Score    FFT(1048576)    SOR(1000*1000)    Monte Carlo    Sparse
matmult(N=100000,nz=1000000)    LU(1000*1000)
243.25    36.42    553.20    34.72    381.71    265.18
278.28    37.74    576.72    39.89    369.94    367.11
266.89    37.42    575.21    41.22    368.48    312.11
271.74    37.63    577.16    39.48    371.28    333.17
269.53    37.49    574.99    41.12    368.88    325.20



gcj-4.0.2 –O3

Composite Score    FFT(1024)    SOR(100*100)    Monte Carlo    Sparse
matmult(N=1000,nz=5000)    LU(100*100)
214.69    228.30    360.18    11.19    151.84    321.94
220.42    195.46    338.18    7.96    276.17    284.33
254.33    214.59    360.18    11.58    277.23    408.05
179.55    184.54    355.71    6.71    143.22    227.56
233.90    215.02    360.58    11.57    276.41    305.92

-large
Composite Score    FFT(1048576)    SOR(1000*1000)    Monte Carlo    Sparse
matmult(N=100000,nz=1000000)    LU(1000*1000)
192.24    29.62    348.23    11.55    222.95    348.86
177.07    35.24    322.72    8.16    232.94    286.25
174.29    35.02    331.95    9.75    249.63    245.09
196.79    27.28    347.29    11.50    255.12    342.76
179.69    37.69    349.346    10.69    176.19    324.57





On 07/03/2008, Aleksey Shipilev <aleksey.shipilev@gmail.com> wrote:
>
> Wow, Simon :)
>
> Can you align this data? It's completely unreadable - I haven't clue
> how Harmony performs looking to these numbers. I'm very interested in
> this measurements.
>
> Thanks,
>
> Aleksey.
>
>
> On Fri, Mar 7, 2008 at 3:39 PM, Simon Chow <simon.harmony@gmail.com>
> wrote:
> > I use a scientific computing benchmark Scimark2, which has 2 running
> modes:
> >  default and -large.
> >  I would like to share it with you. :=)
> >
> >
> >  Platform:
> >  Intel(R) Xeon(TM) CPU 2.80GHz*4.
> >  arch: x86
> >  os: Linux 2.6.18-8.el5xen;
> >  Mem:4GB
> >
> >  Harmony
> >
> >  -Xms1500m -Xmx1500m -Xem:server jnt.scimark2.commandline
> >
> >  Composite Score
> >
> >  FFT
> >
> >  (1024)
> >
> >  SOR
> >
> >  (100*100)
> >
> >  Monte Carlo
> >
> >  Sparse matmult
> >
> >  (N=1000,nz=5000)
> >
> >  LU
> >
> >  (100*100)
> >
> >  193.99
> >
> >  223.91
> >
> >  366.62
> >
> >  28.42
> >
> >  184.19
> >
> >  166.83
> >
> >  194.05
> >
> >  222.20
> >
> >  370.43
> >
> >  28.04
> >
> >  183.16
> >
> >  166.42
> >
> >  193.67
> >
> >  223.05
> >
> >  369.72
> >
> >  28.61
> >
> >  181.29
> >
> >  165.70
> >
> >  193.41
> >
> >  221.29
> >
> >  371.28
> >
> >  27.69
> >
> >  182.04
> >
> >  164.74
> >
> >  194.34
> >
> >  222.48
> >
> >  371.00
> >
> >  28.17
> >
> >  183.32
> >
> >  166.75
> >
> >  -Xms1500m -Xmx1500m -Xem:server jnt.scimark2.commandline -large
> >
> >  Composite Score
> >
> >  FFT
> >
> >  (1048576)
> >
> >  SOR
> >
> >  (1000*1000)
> >
> >  Monte Carlo
> >
> >  Sparse matmult
> >
> >  (N=100000,
> >
> >  nz=1000000)
> >
> >  LU
> >
> >  (1000*1000)
> >
> >  179.31
> >
> >  37.93
> >
> >  359.34
> >
> >  27.18
> >
> >  289.51
> >
> >  182.60
> >
> >  178.31
> >
> >  35.84
> >
> >  359.34
> >
> >  28.08
> >
> >  288.78
> >
> >  179.50
> >
> >  179.35
> >
> >  37.19
> >
> >  258.66
> >
> >  28.08
> >
> >  289.43
> >
> >  183.40
> >
> >  179.02
> >
> >  35.63
> >
> >  360.01
> >
> >  27.14
> >
> >  289.92
> >
> >  182.40
> >
> >  179.80
> >
> >  37.44
> >
> >  360.01
> >
> >  27.25
> >
> >  290.08
> >
> >  184.21
> >
> >
> >  Sun sdk1.5
> >
> >  -Xms1500m -Xmx1500m -server jnt.scimark2.commandline
> >
> >  Composite Score
> >
> >  FFT
> >
> >  (1024)
> >
> >  SOR
> >
> >  (100*100)
> >
> >  Monte Carlo
> >
> >  Sparse matmult
> >
> >  (N=1000,nz=5000)
> >
> >  LU
> >
> >  (100*100)
> >
> >  427.30
> >
> >  252.57
> >
> >  593.82
> >
> >  22.51
> >
> >  321.41
> >
> >  946.18
> >
> >  431.48
> >
> >  272.11
> >
> >  596.21
> >
> >  22.16
> >
> >  322.68
> >
> >  944.21
> >
> >  432.80
> >
> >  273.99
> >
> >  596.77
> >
> >  22.54
> >
> >  322.20
> >
> >  948.48
> >
> >  428.75
> >
> >  256.96
> >
> >  596.03
> >
> >  22.58
> >
> >  323.63
> >
> >  944.54
> >
> >  432.90
> >
> >  276.25
> >
> >  597.32
> >
> >  22.59
> >
> >  323.16
> >
> >  945.19
> >
> >
> >  -Xms1500m -Xmx1500m –server jnt.scimark2.commandline -large
> >
> >  Composite Score
> >
> >  FFT
> >
> >  (1048576)
> >
> >  SOR
> >
> >  (1000*1000)
> >
> >  Monte Carlo
> >
> >  Sparse matmult
> >
> >  (N=100000,
> >
> >  nz=1000000)
> >
> >  LU
> >
> >  (1000*1000)
> >
> >  243.25
> >
> >  36.42
> >
> >  553.20
> >
> >  34.72
> >
> >  381.71
> >
> >  265.18
> >
> >  278.28
> >
> >  37.74
> >
> >  576.72
> >
> >  39.89
> >
> >  369.94
> >
> >  367.11
> >
> >  266.89
> >
> >  37.42
> >
> >  575.21
> >
> >  41.22
> >
> >  368.48
> >
> >  312.11
> >
> >  271.74
> >
> >  37.63
> >
> >  577.16
> >
> >  39.48
> >
> >  371.28
> >
> >  333.17
> >
> >  269.53
> >
> >  37.49
> >
> >  574.99
> >
> >  41.12
> >
> >  368.88
> >
> >  325.20
> >
> >
> >  gcj-4.0.2 –O3
> >
> >  Composite Score
> >
> >  FFT
> >
> >  (1024)
> >
> >  SOR
> >
> >  (100*100)
> >
> >  Monte Carlo
> >
> >  Sparse matmult
> >
> >  (N=1000,
> >
> >  nz=5000)
> >
> >  LU
> >
> >  (100*100)
> >
> >  214.69
> >
> >  228.30
> >
> >  360.18
> >
> >  11.19
> >
> >  151.84
> >
> >  321.94
> >
> >  220.42
> >
> >  195.46
> >
> >  338.18
> >
> >  7.96
> >
> >  276.17
> >
> >  284.33
> >
> >  254.33
> >
> >  214.59
> >
> >  360.18
> >
> >  11.58
> >
> >  277.23
> >
> >  408.05
> >
> >  179.55
> >
> >  184.54
> >
> >  355.71
> >
> >  6.71
> >
> >  143.22
> >
> >  227.56
> >
> >  233.90
> >
> >  215.02
> >
> >  360.58
> >
> >  11.57
> >
> >  276.41
> >
> >  305.92
> >
> >  -large
> >
> >  Composite Score
> >
> >  FFT
> >
> >  (1048576)
> >
> >  SOR
> >
> >  (1000*1000)
> >
> >  Monte Carlo
> >
> >  Sparse matmult
> >
> >  (N=100000,
> >
> >  nz=1000000)
> >
> >  LU
> >
> >  (1000*1000)
> >
> >  192.24
> >
> >  29.62
> >
> >  348.23
> >
> >  11.55
> >
> >  222.95
> >
> >  348.86
> >
> >  177.07
> >
> >  35.24
> >
> >  322.72
> >
> >  8.16
> >
> >  232.94
> >
> >  286.25
> >
> >  174.29
> >
> >  35.02
> >
> >  331.95
> >
> >  9.75
> >
> >  249.63
> >
> >  245.09
> >
> >  196.79
> >
> >  27.28
> >
> >  347.29
> >
> >  11.50
> >
> >  255.12
> >
> >  342.76
> >
> >  179.69
> >
> >  37.69
> >
> >  349.346
> >
> >  10.69
> >
> >  176.19
> >
> >  324.57
> >
> >
> >
> >  --
> >  From : Simon.Chow@Software School of Fudan University
> >
>



-- 
>From : Simon.Chow@Software School of Fudan University

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