Author: Carl Friedrich Bolz <[email protected]> Branch: extradoc Changeset: r4677:60a87d49ded7 Date: 2012-08-17 14:28 +0200 http://bitbucket.org/pypy/extradoc/changeset/60a87d49ded7/
Log: clean up the various result files (we have version control) diff --git a/talk/dls2012/benchmarks/new_result.txt b/talk/dls2012/benchmarks/new_result.txt deleted file mode 100644 --- a/talk/dls2012/benchmarks/new_result.txt +++ /dev/null @@ -1,106 +0,0 @@ - -pypy -sqrt(int): 1.81961710453 +- 0.00969663499951 -sqrt(float): 0.997122144699 +- 0.00475528903922 -sqrt(Fix16): 2.14047310352 +- 0.0175369211294 -conv3(1e6): 0.765250277519 +- 0.0111246299589 -conv5(1e6): 1.08676469326 +- 0.0181131040106 -conv3(1e5): 0.675209879875 +- 0.0210395038414 -conv5(1e5): 1.05374486446 +- 0.0284513681407 -conv3x3(3): 0.0678671360016 +- 0.00108163728271 -conv3x3(1000): 0.0530683040619 +- 0.0344658980996 -dilate3x3(1000): 0.389708518982 +- 0.00835149413747 -NoBorderImagePadded: 1.93399097919 +- 0.0524961558513 -NoBorderImagePadded(iter): 0.488634562492 +- 0.0171516205712 -NoBorderImagePadded(range): 0.483622479439 +- 0.00925072290815 -NoBorderImage: 2.16889901161 +- 0.0157656334579 -NoBorderImage(iter): 1.47057991028 +- 0.0233604904862 -NoBorderImage(range): 1.39746711254 +- 0.0358702404701 -sobel(NoBorderImagePadded): 0.47727098465 +- 0.0285302209995 -sobel_uint8(NoBorderImagePadded): 0.513068723679 +- 0.00450907878019 - -pypy --jit enable_opts=intbounds:rewrite:virtualize:heap -sqrt(int): 2.26462423801 +- 0.0076627615314 -sqrt(float): 1.35695979595 +- 0.0251587469884 -sqrt(Fix16): 3.93270061016 +- 0.109339327977 -conv3(1e6): 1.68973388672 +- 0.0142045606781 -conv5(1e6): 1.92141816616 +- 0.034837452752 -conv3(1e5): 1.77114777565 +- 0.0558894026315 -conv5(1e5): 1.86009068489 +- 0.0184543492536 -conv3x3(3): 0.0988693475723 +- 0.00115722747303 -conv3x3(1000): 0.0734650850296 +- 0.00267271135671 -dilate3x3(1000): 0.411496067047 +- 0.035852331563 -NoBorderImagePadded: 2.09047472477 +- 0.117371924965 -NoBorderImagePadded(iter): 1.2149545908 +- 0.0217855739412 -NoBorderImagePadded(range): 1.11978774071 +- 0.0280553099539 -NoBorderImage: 2.22395954132 +- 0.0316863806008 -NoBorderImage(iter): 1.44512989521 +- 0.0304946877295 -NoBorderImage(range): 1.34203736782 +- 0.0314288487567 -sobel(NoBorderImagePadded): 1.01348490715 +- 0.0263135905465 -sobel_uint8(NoBorderImagePadded): 1.04967999458 +- 0.0124143422099 - -gcc -O2 -sqrt(float): 0.98 +- 1.24126707662e-16 -sqrt(int): 0.806 +- 0.00894427191 -sqrt(Fix16): 0.972 +- 0.01788854382 -conv3(1e6): 0.84 +- 0.0452769256907 -conv5(1e6): 1.074 +- 0.0517687164222 -conv3(1e5): 0.702 +- 0.0465832587954 -conv5(1e5): 1.03 +- 0.0484767985742 -conv3x3(3): 0.274 +- 0.00894427191 -conv3x3(1000): 0.242 +- 0.004472135955 -dilate3x3(1000): 0.258 +- 0.004472135955 -sobel_magnitude: 0.194 +- 0.00894427191 - -gcc -O3 -march=native -fno-tree-vectorize -sqrt(float): 0.98 +- 1.24126707662e-16 -sqrt(int): 0.804 +- 0.00894427191 -sqrt(Fix16): 0.96 +- 0.0122474487139 -conv3(1e6): 0.744 +- 0.011401754251 -conv5(1e6): 0.8 +- 0.0122474487139 -conv3(1e5): 0.588 +- 0.0130384048104 -conv5(1e5): 0.65 +- 0.0122474487139 -conv3x3(3): 0.274 +- 0.00547722557505 -conv3x3(1000): 0.25 +- 0.00707106781187 -dilate3x3(1000): 0.256 +- 0.00894427191 -sobel_magnitude: 0.2 +- 0.0141421356237 - -python2.7 -sqrt(int): 20.8419699669 -sqrt(float): 24.2056779861 -sqrt(Fix16): 744.34590292 -conv3(1e6): 77.1459159851 -conv5(1e6): 125.768272161 -conv3(1e5): 77.8904190063 -conv5(1e5): 122.540805101 -conv3x3(3): 23.8474378586 -conv3x3(1000): 23.7241849899 -dilate3x3(1000): 23.2892370224 -NoBorderImagePadded: 543.731127977 -NoBorderImagePadded(iter): 546.704558849 -NoBorderImagePadded(range): 550.923794985 -NoBorderImage: 537.306480885 -NoBorderImage(iter): 548.317567825 -NoBorderImage(range): 534.642185926 -sobel(NoBorderImagePadded): 461.142298937 -sobel_uint8(NoBorderImagePadded): 476.717667103 - -python2.6 psyco-wrapper.py -sqrt(int): 1.77652692795 -sqrt(float): 5.52010679245 -sqrt(Fix16): 421.651717901 -conv3(1e6): 9.58111596107 -conv5(1e6): 16.7954330444 -conv3(1e5): 9.51570010185 -conv5(1e5): 16.6677658558 -conv3x3(3): 12.7717211246 -conv3x3(1000): 12.7678999901 -dilate3x3(1000): 12.9881358147 -NoBorderImagePadded: 333.201485157 -NoBorderImagePadded(iter): 309.316030979 -NoBorderImagePadded(range): 318.333670855 -NoBorderImage: 329.979980946 -NoBorderImage(iter): 304.132736921 -NoBorderImage(range): 317.337441921 -sobel(NoBorderImagePadded): 258.021892071 -sobel_uint8(NoBorderImagePadded): 275.499665976 diff --git a/talk/dls2012/benchmarks/result.txt b/talk/dls2012/benchmarks/result.txt deleted file mode 100644 --- a/talk/dls2012/benchmarks/result.txt +++ /dev/null @@ -1,189 +0,0 @@ - -pypy -sqrt(int): 3.9497149229 +- 0.00120169176702 -sqrt(float): 1.18568074703 +- 0.000155574177096 -sqrt(Fix16): 4.33989310265 +- 0.00141233338935 -conv3(array(1e6)): 0.509183955193 +- 0.0118453357313 -conv5(array(1e6)): 0.69121158123 +- 0.00750138546764 -conv3(array(1e5)): 0.4399548769 +- 0.00179808936191 -conv5(array(1e5)): 0.641533112526 +- 0.00283121562299 -conv3x3(Array2D(1000000x3)): 0.32311899662 +- 0.00297940582696 -conv3x3(Array2D(1000x1000)): 0.294556212425 +- 0.00394363604342 -dilate3x3(Array2D(1000x1000)): 5.62028222084 +- 0.0100742850395 -sobel(Array2D(1000x1000)): 0.353349781036 +- 0.000422230713013 -SOR(100, 32768): 3.6967458725 +- 0.00479411350316 -SOR(1000, 256): 2.92602846622 +- 0.00460152567878 -SOR(100, 32768): 5.91232867241 +- 0.0575417343725 -SOR(1000, 256): 4.48931508064 +- 0.0545822457385 -SparseMatMult(1000, 5000, 262144): 45.573383832 +- 0.628020354674 -SparseMatMult(100000, 1000000, 1024): 31.8840100527 +- 0.0835424264131 -MonteCarlo(268435456): 18.0108832598 +- 0.0590538416431 -LU(100, 4096): 17.11741395 +- 0.146651016873 -LU(1000, 2): 8.36587500572 +- 0.0643368943091 - -pypy --jit enable_opts=intbounds:rewrite:virtualize:string:earlyforce:pure:heap:ffi -sqrt(int): 5.38412702084 +- 0.0100677718267 -sqrt(float): 2.49882881641 +- 0.000611829128708 -sqrt(Fix16): 9.08926799297 +- 0.00638996685205 -conv3(array(1e6)): 2.07706921101 +- 0.0578137268002 -conv5(array(1e6)): 2.29385373592 +- 0.239051363255 -conv3(array(1e5)): 1.9695744276 +- 0.00699373341986 -conv5(array(1e5)): 2.06334021091 +- 0.00461312422073 -conv3x3(Array2D(1000000x3)): 0.913360571861 +- 0.00406856919645 -conv3x3(Array2D(1000x1000)): 0.906745815277 +- 0.011800811341 -dilate3x3(Array2D(1000x1000)): 5.94119987488 +- 0.0177689080267 -sobel(Array2D(1000x1000)): 0.879287624359 +- 0.00351199656947 -SOR(100, 32768): 13.3457442522 +- 0.15597493782 -SOR(1000, 256): 10.6485268593 +- 0.0335292228831 -SOR(100, 32768): 15.2722632885 +- 0.149270948773 -SOR(1000, 256): 12.2542063951 +- 0.0467913588079 -SparseMatMult(1000, 5000, 262144): 51.7010503292 +- 0.0900830635215 -SparseMatMult(100000, 1000000, 1024): 34.0754101276 +- 0.0854521241748 -MonteCarlo(268435456): 27.4164168119 +- 0.00974970184296 -LU(100, 4096): 48.2948143244 +- 0.509639206256 -LU(1000, 2): 24.4584824085 +- 0.0807806236077 - -pypy-1.5 -sqrt(int): 4.01375324726 +- 0.0011476694851 -sqrt(float): 1.18687217236 +- 0.000301798978394 -sqrt(Fix16): 4.86933817863 +- 0.00205854686543 -conv3(array(1e6)): 0.805051374435 +- 0.0063356172758 -conv5(array(1e6)): 1.06881151199 +- 0.166557589133 -conv3(array(1e5)): 0.767954874039 +- 0.00310620949945 -conv5(array(1e5)): 0.965079665184 +- 0.000806628058215 -conv3x3(Array2D(1000000x3)): 0.335144019127 +- 0.00049856745349 -conv3x3(Array2D(1000x1000)): 0.29465200901 +- 0.000517387744409 -dilate3x3(Array2D(1000x1000)): 4.75037336349 +- 0.0580217877578 -sobel(Array2D(1000x1000)): 0.663321614265 +- 0.122793251782 -SOR(100, 32768): 4.81084053516 +- 0.00994169505717 -SOR(1000, 256): 3.69062592983 +- 0.000879615350989 -SparseMatMult(1000, 5000, 262144): 29.4872629166 +- 0.10046773485 -SparseMatMult(100000, 1000000, 1024): 16.4197937727 +- 0.0719696247072 -MonteCarlo(268435456): 33.0701499462 +- 0.0638672466435 - -pypy-1.5 --jit enable_opts=intbounds:rewrite:virtualize:heap -sqrt(int): 4.90680310726 +- 0.0163989281435 -sqrt(float): 1.76404910088 +- 0.019897073087 -sqrt(Fix16): 9.64484581947 +- 0.114181653484 -conv3(array(1e6)): 2.09028859138 +- 0.0553368910699 -conv5(array(1e6)): 1.98986980915 +- 0.0147589410577 -conv3(array(1e5)): 2.03130574226 +- 0.0153185288294 -conv5(array(1e5)): 1.95361895561 +- 0.00846210060946 -conv3x3(Array2D(1000000x3)): 0.771404409409 +- 0.00438046479707 -conv3x3(Array2D(1000x1000)): 0.724743962288 +- 0.00330094765836 -dilate3x3(Array2D(1000x1000)): 4.96963682175 +- 0.00698590266664 -sobel(Array2D(1000x1000)): 1.63008458614 +- 1.3629432655 -SOR(100, 32768): 13.871041584 +- 0.0322488434431 -SOR(1000, 256): 11.9500208616 +- 0.00961527429654 -SparseMatMult(1000, 5000, 262144): 37.7395636082 +- 0.108390387625 -SparseMatMult(100000, 1000000, 1024): 27.7381374121 +- 0.105548816891 -MonteCarlo(268435456): 30.6472777128 +- 0.0437974003055 - -gcc -O3 -march=native -fno-tree-vectorize -sqrt(float): 1.14 +- 0.0 -sqrt(int): 1.85 +- 0.0 -sqrt(Fix16): 1.992 +- 0.004472135955 -conv3(1e6): 1.066 +- 0.00547722557505 -conv5(1e6): 1.104 +- 0.00547722557505 -conv3(1e5): 0.75 +- 0.0 -conv5(1e5): 1.03 +- 0.0 -conv3x3(3): 0.22 +- 3.10316769156e-17 -conv3x3(1000): 0.2 +- 0.0 -dilate3x3(1000): 0.2 +- 0.0 -SOR(100,32768): 2.506 +- 0.00547722557505 -SOR(1000,256): 2.072 +- 0.004472135955 -SparseMatMult(1000,5000,262144): 2.54 +- 0.0 -SparseMatMult(100000,1000000,1024): 2.398 +- 0.004472135955 -MonteCarlo(268435456): 2.52 +- 0.0 -LU(100,4096): 1.882 +- 0.004472135955 -LU(1000,2): 2.036 +- 0.00547722557505 - -python2.7 -sqrt(int): 15.5302910805 -sqrt(float): 19.8081839085 -sqrt(Fix16): 690.281599045 -conv3(array(1e6)): 58.9430649281 -conv5(array(1e6)): 88.9902608395 -conv3(array(1e5)): 60.0520131588 -conv5(array(1e5)): 88.7499320507 -conv3x3(Array2D(1000000x3)): 182.564875841 -conv3x3(Array2D(1000x1000)): 179.802839994 -dilate3x3(Array2D(1000x1000)): 177.197051048 -sobel(Array2D(1000x1000)): 132.991428852 -SOR(100, 32768): 1854.50835085 -SOR(1000, 256): 1506.28460383 -SOR(100, 32768): 1279.75841594 -SOR(1000, 256): 1038.63221002 -SparseMatMult(1000, 5000, 262144): 456.105548859 -SparseMatMult(100000, 1000000, 1024): 272.003329039 -MonteCarlo(268435456): 800.114681005 -LU(100, 4096): 2704.15891314 -LU(1000, 2): 1317.06345105 - -python2.6 psyco-wrapper.py - -luajit-2.0.0-beta10 -sqrt(int): 1.185000 +- 0.005270 -sqrt(float): 1.185000 +- 0.005270 -sqrt(Fix16): 106.936000 +- 0.350213 -convolution(conv3): 0.476000 +- 0.005164 -convolution(conv5): 0.478000 +- 0.012293 -convolution(conv3): 0.172000 +- 0.006325 -convolution(conv5): 0.286000 +- 0.005164 -convolution(conv3x3): 0.207000 +- 0.004830 -convolution(conv3x3): 0.167000 +- 0.006749 -convolution(dilate3x3): 0.165000 +- 0.005270 -convolution(sobel_magnitude): 0.398000 +- 0.006325 -SOR(100, 32768): 2.186000 +- 0.005164 -SOR(1000, 256): 1.797000 +- 0.006749 -SparseMatMult(1000,5000,262144): 6.642000 +- 0.049621 -SparseMatMult(100000,1000000,1024): 3.846000 +- 0.023664 -MonteCarlo(268435456): 4.082000 +- 0.004216 -LU(100, 4096): 2.371000 +- 0.019120 -LU(1000, 2): 2.141000 +- 0.037550 -FFT(1024, 32768): 3.900000 +- 0.010541 -FFT(1048576, 2): 2.815000 +- 0.142848 - -luajit-2.0.0-beta10 -O-loop -sqrt(int): 1.462000 +- 0.004216 -sqrt(float): 1.462000 +- 0.004216 -sqrt(Fix16): 102.775000 +- 0.332106 -convolution(conv3): 0.950000 +- 0.006667 -convolution(conv5): 1.219000 +- 0.077093 -convolution(conv3): 0.894000 +- 0.005164 -convolution(conv5): 1.150000 +- 0.004714 -convolution(conv3x3): 0.734000 +- 0.005164 -convolution(conv3x3): 0.691000 +- 0.007379 -convolution(dilate3x3): 0.710000 +- 0.012472 -convolution(sobel_magnitude): 0.833000 +- 0.009487 -SOR(100, 32768): 2.727000 +- 0.004830 -SOR(1000, 256): 2.264000 +- 0.005164 -SparseMatMult(1000,5000,262144): 13.485000 +- 0.235384 -SparseMatMult(100000,1000000,1024): 10.869000 +- 0.014491 -MonteCarlo(268435456): 5.943000 +- 0.006749 -LU(100, 4096): 11.064000 +- 0.019551 -LU(1000, 2): 5.109000 +- 0.005676 -FFT(1024, 32768): 5.999000 +- 0.007379 -FFT(1048576, 2): 2.997000 +- 0.137602 - -luajit-master -sqrt(int): 1.185000 +- 0.005270 -sqrt(float): 1.185000 +- 0.005270 -sqrt(Fix16): 1.739000 +- 0.003162 -convolution(conv3): 0.477000 +- 0.008233 -convolution(conv5): 0.474000 +- 0.005164 -convolution(conv3): 0.165000 +- 0.005270 -convolution(conv5): 0.286000 +- 0.005164 -convolution(conv3x3): 0.207000 +- 0.004830 -convolution(conv3x3): 0.167000 +- 0.006749 -convolution(dilate3x3): 0.163000 +- 0.006749 -convolution(sobel_magnitude): 0.403000 +- 0.009487 -SOR(100, 32768): 2.187000 +- 0.006749 -SOR(1000, 256): 1.802000 +- 0.006325 -SparseMatMult(1000,5000,262144): 6.683000 +- 0.029833 -SparseMatMult(100000,1000000,1024): 3.870000 +- 0.037712 -MonteCarlo(268435456): 4.035000 +- 0.005270 -LU(100, 4096): 2.351000 +- 0.008756 -LU(1000, 2): 2.107000 +- 0.018288 -FFT(1024, 32768): 3.926000 +- 0.010750 -FFT(1048576, 2): 2.865000 +- 0.064334 diff --git a/talk/dls2012/benchmarks/results-lua b/talk/dls2012/benchmarks/results-lua deleted file mode 100644 --- a/talk/dls2012/benchmarks/results-lua +++ /dev/null @@ -1,44 +0,0 @@ - -luajit -sqrt(int): 0.834000 +- 0.006992 -sqrt(float): 0.834000 +- 0.006992 -sqrt(Fix16): 1.474000 +- 0.006992 -conv3(array(1e6)): 0.177000 +- 0.004830 -conv5(array(1e6)): 0.212000 +- 0.009189 -conv3(array(1e5)): 0.124000 +- 0.005164 -conv5(array(1e5)): 0.174000 +- 0.005164 -conv3x3(Array2D(1000000x3)): 0.092000 +- 0.006325 -conv3x3(Array2D(1000x1000)): 0.154000 +- 0.005164 -dilate3x3(Array2D(1000x1000)): 0.156000 +- 0.005164 -sobel(Array2D(1000x1000)): 0.239000 +- 0.008756 -SOR(100, 32768): 1.314000 +- 0.005164 -SOR(1000, 256): 1.076000 +- 0.006992 -SparseMatMult(1000,5000,262144): 4.489000 +- 0.018529 -SparseMatMult(100000,1000000,1024): 2.433000 +- 0.015670 -MonteCarlo(268435456): 2.824000 +- 0.005164 -LU(100, 4096): 1.524000 +- 0.005164 -LU(1000, 2): 0.665000 +- 0.007071 -FFT(1024, 32768): 2.740000 +- 0.010541 -FFT(1048576, 2): 1.071000 +- 0.025582 - -luajit -O-loop -sqrt(int): 1.057000 +- 0.004830 -sqrt(float): 1.056000 +- 0.005164 -sqrt(Fix16): 3.998000 +- 0.020440 -conv3(array(1e6)): 0.697000 +- 0.004830 -conv5(array(1e6)): 0.864000 +- 0.006992 -conv3(array(1e5)): 0.673000 +- 0.004830 -conv5(array(1e5)): 0.840000 +- 0.004714 -conv3x3(Array2D(1000000x3)): 0.141000 +- 0.005676 -conv3x3(Array2D(1000x1000)): 0.217000 +- 0.004830 -dilate3x3(Array2D(1000x1000)): 0.222000 +- 0.004216 -sobel(Array2D(1000x1000)): 0.366000 +- 0.006992 -SOR(100, 32768): 2.019000 +- 0.005676 -SOR(1000, 256): 1.629000 +- 0.003162 -SparseMatMult(1000,5000,262144): 9.690000 +- 0.016997 -SparseMatMult(100000,1000000,1024): 7.191000 +- 0.009944 -MonteCarlo(268435456): 3.923000 +- 0.006749 -LU(100, 4096): 8.570000 +- 0.009428 -LU(1000, 2): 4.002000 +- 0.009189 -FFT(1024, 32768): 4.454000 +- 0.009661 -FFT(1048576, 2): 1.253000 +- 0.009487 diff --git a/talk/dls2012/benchmarks/results-newer b/talk/dls2012/benchmarks/results-newer --- a/talk/dls2012/benchmarks/results-newer +++ b/talk/dls2012/benchmarks/results-newer @@ -89,44 +89,44 @@ luajit sqrt(int): 0.834000 +- 0.006992 -sqrt(float): 0.834000 +- 0.005164 -sqrt(Fix16): 1.140000 +- 0.004714 -conv3(1e6): 0.180000 +- 0.000000 -conv5(1e6): 0.210000 +- 0.006667 -conv3(1e5): 0.124000 +- 0.005164 -conv5(1e5): 0.175000 +- 0.005270 -conv3x3(1000000, 3): 0.127000 +- 0.004830 -conv3x3(1000, 1000): 0.094000 +- 0.005164 -dilate3x3(1000, 1000): 0.091000 +- 0.003162 -sobel(Array2D(1000x1000)): 0.238000 +- 0.009189 +sqrt(float): 0.834000 +- 0.006992 +sqrt(Fix16): 1.474000 +- 0.006992 +conv3(array(1e6)): 0.177000 +- 0.004830 +conv5(array(1e6)): 0.212000 +- 0.009189 +conv3(array(1e5)): 0.124000 +- 0.005164 +conv5(array(1e5)): 0.174000 +- 0.005164 +conv3x3(Array2D(1000000x3)): 0.092000 +- 0.006325 +conv3x3(Array2D(1000x1000)): 0.154000 +- 0.005164 +dilate3x3(Array2D(1000x1000)): 0.156000 +- 0.005164 +sobel(Array2D(1000x1000)): 0.239000 +- 0.008756 SOR(100, 32768): 1.314000 +- 0.005164 -SOR(1000, 256): 1.076000 +- 0.005164 -SparseMatMult(1000,5000,262144): 4.528000 +- 0.016193 -SparseMatMult(100000,1000000,1024): 2.416000 +- 0.005164 -MonteCarlo(268435456): 2.823000 +- 0.004830 -LU(100, 4096): 1.524000 +- 0.006992 -LU(1000, 2): 0.665000 +- 0.005270 -FFT(1024, 32768): 2.764000 +- 0.008433 -FFT(1048576, 2): 1.085000 +- 0.007071 +SOR(1000, 256): 1.076000 +- 0.006992 +SparseMatMult(1000,5000,262144): 4.489000 +- 0.018529 +SparseMatMult(100000,1000000,1024): 2.433000 +- 0.015670 +MonteCarlo(268435456): 2.824000 +- 0.005164 +LU(100, 4096): 1.524000 +- 0.005164 +LU(1000, 2): 0.665000 +- 0.007071 +FFT(1024, 32768): 2.740000 +- 0.010541 +FFT(1048576, 2): 1.071000 +- 0.025582 luajit -O-loop sqrt(int): 1.057000 +- 0.004830 -sqrt(float): 1.057000 +- 0.006749 -sqrt(Fix16): 12.802000 +- 0.040770 -conv3(1e6): 0.702000 +- 0.004216 -conv5(1e6): 0.866000 +- 0.005164 -conv3(1e5): 0.674000 +- 0.005164 -conv5(1e5): 0.841000 +- 0.003162 -conv3x3(1000000, 3): 0.528000 +- 0.004216 -conv3x3(1000, 1000): 0.495000 +- 0.005270 -dilate3x3(1000, 1000): 0.484000 +- 0.006992 -sobel(Array(1000x1000)): 0.602000 +- 0.006325 -SOR(100, 32768): 2.020000 +- 0.004714 -SOR(1000, 256): 1.630000 +- 0.004714 -SparseMatMult(1000,5000,262144): 9.637000 +- 0.016364 -SparseMatMult(100000,1000000,1024): 7.187000 +- 0.008233 -MonteCarlo(268435456): 3.923000 +- 0.008233 -LU(100, 4096): 8.568000 +- 0.006325 -LU(1000, 2): 3.994000 +- 0.006992 -FFT(1024, 32768): 4.425000 +- 0.008498 -FFT(1048576, 2): 1.326000 +- 0.014298 +sqrt(float): 1.056000 +- 0.005164 +sqrt(Fix16): 3.998000 +- 0.020440 +conv3(array(1e6)): 0.697000 +- 0.004830 +conv5(array(1e6)): 0.864000 +- 0.006992 +conv3(array(1e5)): 0.673000 +- 0.004830 +conv5(array(1e5)): 0.840000 +- 0.004714 +conv3x3(Array2D(1000000x3)): 0.141000 +- 0.005676 +conv3x3(Array2D(1000x1000)): 0.217000 +- 0.004830 +dilate3x3(Array2D(1000x1000)): 0.222000 +- 0.004216 +sobel(Array2D(1000x1000)): 0.366000 +- 0.006992 +SOR(100, 32768): 2.019000 +- 0.005676 +SOR(1000, 256): 1.629000 +- 0.003162 +SparseMatMult(1000,5000,262144): 9.690000 +- 0.016997 +SparseMatMult(100000,1000000,1024): 7.191000 +- 0.009944 +MonteCarlo(268435456): 3.923000 +- 0.006749 +LU(100, 4096): 8.570000 +- 0.009428 +LU(1000, 2): 4.002000 +- 0.009189 +FFT(1024, 32768): 4.454000 +- 0.009661 +FFT(1048576, 2): 1.253000 +- 0.009487 _______________________________________________ pypy-commit mailing list [email protected] http://mail.python.org/mailman/listinfo/pypy-commit
