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 <[EMAIL PROTECTED]> 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 <[EMAIL PROTECTED]> > 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 : [EMAIL PROTECTED] School of Fudan University > > > -- >From : [EMAIL PROTECTED] School of Fudan University
