[pypy-commit] extradoc extradoc: regenerate table

[cfbolz]

Author: Carl Friedrich Bolz <cfbolz at gmx.de>
Branch: extradoc
Changeset: r4643:6e7dbbbfaf4d
Date: 2012-08-16 19:29 +0200
http://bitbucket.org/pypy/extradoc/changeset/6e7dbbbfaf4d/

Log:	regenerate table

diff --git a/talk/dls2012/paper.tex b/talk/dls2012/paper.tex
--- a/talk/dls2012/paper.tex
+++ b/talk/dls2012/paper.tex
@@ -907,59 +907,55 @@
 {\smaller
 \begin{tabular}{|l|r|r|r|r|r|r|r|}
 \hline
- & CPython & PyPy  & PyPy & GCC & luajit & luajit \\
- &        & no LP &       & -O3 &        & no LP \\
-FFT(1024,32768) & 469.07 & 20.83 +- 0.02 & 12.73 +- 0.01 & 1.40 +- 0.04 & 2.76 +- 0.01 & 4.42 +- 0.01\\
+ & CPython & PyPy  & PyPy & LuaJIT & LuaJIT & GCC \\
+ &         & no LP &      & no LP  &        & -O3 \\
 \hline
-FFT(1048576,2) & 58.93 & 4.12 +- 0.01 & 2.05 +- 0.00 & 0.83 +- 0.02 & 1.08 +- 0.01 & 1.33 +- 0.01\\
+FFT(1024,32768) & 469.07 & 20.83 $\pm$ 0.039 & 12.73 $\pm$ 0.029 & 4.42 $\pm$ 0.017 & 2.76 $\pm$ 0.017 & 1.40 $\pm$ 0.082\\
 \hline
-LU(100,4096) & 1974.14 & 32.22 +- 0.14 & 13.39 +- 0.03 & 1.33 +- 0.04 & 1.52 +- 0.01 & 8.57 +- 0.01\\
+FFT(1048576,2) & 58.93 & 4.12 $\pm$ 0.020 & 2.05 $\pm$ 0.007 & 1.33 $\pm$ 0.028 & 1.08 $\pm$ 0.014 & 0.83 $\pm$ 0.044\\
 \hline
-LU(1000,2) & 955.31 & 14.98 +- 0.22 & 5.99 +- 0.21 & 0.65 +- 0.04 & 0.67 +- 0.01 & 3.99 +- 0.01\\
+LU(100,4096) & 1974.14 & 32.22 $\pm$ 0.281 & 13.39 $\pm$ 0.063 & 8.57 $\pm$ 0.012 & 1.52 $\pm$ 0.014 & 1.33 $\pm$ 0.070\\
 \hline
-MonteCarlo(268435456) & 618.89 & 20.60 +- 0.05 & 15.33 +- 0.08 & 1.69 +- 0.05 & 2.82 +- 0.00 & 3.92 +- 0.01\\
+LU(1000,2) & 955.31 & 14.98 $\pm$ 0.436 & 5.99 $\pm$ 0.416 & 3.99 $\pm$ 0.014 & 0.67 $\pm$ 0.010 & 0.65 $\pm$ 0.077\\
 \hline
-SOR(100,32768) & 1458.12 & 8.24 +- 0.00 & 2.66 +- 0.00 & 1.76 +- 0.04 & 1.31 +- 0.01 & 2.02 +- 0.00\\
+MonteCarlo(268435456) & 618.89 & 20.60 $\pm$ 0.097 & 15.33 $\pm$ 0.163 & 3.92 $\pm$ 0.016 & 2.82 $\pm$ 0.009 & 1.69 $\pm$ 0.096\\
 \hline
-SOR(1000,256) & 1210.45 & 6.48 +- 0.00 & 2.10 +- 0.00 & 1.49 +- 0.02 & 1.08 +- 0.01 & 1.63 +- 0.00\\
+SOR(100,32768) & 1458.12 & 8.24 $\pm$ 0.002 & 2.66 $\pm$ 0.002 & 2.02 $\pm$ 0.009 & 1.31 $\pm$ 0.010 & 1.76 $\pm$ 0.088\\
 \hline
-SparseMatMult(1000,5000,262144) & 371.66 & 24.25 +- 0.04 & 16.52 +- 0.04 & 1.84 +- 0.03 & 4.53 +- 0.02 & 9.64 +- 0.02\\
+SOR(1000,256) & 1210.45 & 6.48 $\pm$ 0.007 & 2.10 $\pm$ 0.005 & 1.63 $\pm$ 0.009 & 1.08 $\pm$ 0.010 & 1.49 $\pm$ 0.042\\
 \hline
-SparseMatMult(100000,1000000,1024) & 236.93 & 17.01 +- 0.01 & 8.75 +- 0.08 & 1.20 +- 0.03 & 2.42 +- 0.01 & 7.19 +- 0.01\\
+SparseMatMult(1000,5000,262144) & 371.66 & 24.25 $\pm$ 0.074 & 16.52 $\pm$ 0.077 & 9.64 $\pm$ 0.032 & 4.53 $\pm$ 0.032 & 1.84 $\pm$ 0.061\\
 \hline
-conv3(1e5) & 50.14 & 1.09 +- 0.01 & 0.49 +- 0.01 & 0.52 +- 0.04 & 0.12 +- 0.01 & 0.67 +- 0.01\\
+SparseMatMult(100000,1000000,1024) & 236.93 & 17.01 $\pm$ 0.025 & 8.75 $\pm$ 0.149 & 7.19 $\pm$ 0.016 & 2.42 $\pm$ 0.010 & 1.20 $\pm$ 0.053\\
 \hline
-conv3(1e6) & 49.20 & 1.13 +- 0.02 & 0.51 +- 0.00 & 0.60 +- 0.03 & 0.18 +- 0.00 & 0.70 +- 0.00\\
 \hline
-conv3x3(1000) & - & - & - & 0.17 +- 0.04 & - & -\\
+conv3(1e5) & 50.14 & 1.09 $\pm$ 0.022 & 0.49 $\pm$ 0.028 & 0.67 $\pm$ 0.010 & 0.12 $\pm$ 0.010 & 0.52 $\pm$ 0.084\\
 \hline
-conv3x3(1000,1000) & 138.95 & 0.70 +- 0.00 & 0.20 +- 0.00 & - & 0.09 +- 0.01 & 0.49 +- 0.01\\
+conv3(1e6) & 49.20 & 1.13 $\pm$ 0.043 & 0.51 $\pm$ 0.008 & 0.70 $\pm$ 0.008 & 0.18 $\pm$ 0.000 & 0.60 $\pm$ 0.064\\
 \hline
-conv3x3(1000000,3) & 139.81 & 0.70 +- 0.00 & 0.21 +- 0.00 & - & 0.13 +- 0.00 & 0.53 +- 0.00\\
+conv3x3(1000,1000) & 138.95 & 0.70 $\pm$ 0.007 & 0.20 $\pm$ 0.009 & 0.49 $\pm$ 0.010 & 0.09 $\pm$ 0.010 & 0.17 $\pm$ 0.079\\
 \hline
-conv3x3(3) & - & - & - & 0.19 +- 0.03 & - & -\\
+conv3x3(1000000,3) & 139.81 & 0.70 $\pm$ 0.005 & 0.21 $\pm$ 0.006 & 0.53 $\pm$ 0.008 & 0.13 $\pm$ 0.009 & 0.19 $\pm$ 0.061\\
 \hline
-conv5(1e5) & 74.65 & 1.22 +- 0.00 & 0.64 +- 0.00 & 0.55 +- 0.02 & 0.17 +- 0.01 & 0.84 +- 0.00\\
+conv5(1e5) & 74.65 & 1.22 $\pm$ 0.009 & 0.64 $\pm$ 0.005 & 0.84 $\pm$ 0.006 & 0.17 $\pm$ 0.010 & 0.55 $\pm$ 0.047\\
 \hline
-conv5(1e6) & 77.94 & 1.26 +- 0.00 & 0.68 +- 0.01 & 0.58 +- 0.03 & 0.21 +- 0.01 & 0.87 +- 0.01\\
+conv5(1e6) & 77.94 & 1.26 $\pm$ 0.009 & 0.68 $\pm$ 0.014 & 0.87 $\pm$ 0.010 & 0.21 $\pm$ 0.013 & 0.58 $\pm$ 0.049\\
 \hline
-dilate3x3(1000) & - & - & - & 0.17 +- 0.03 & - & -\\
+dilate3x3(1000,1000) & 137.52 & 4.35 $\pm$ 0.014 & 3.91 $\pm$ 0.037 & 0.48 $\pm$ 0.014 & 0.09 $\pm$ 0.006 & 0.17 $\pm$ 0.061\\
 \hline
-dilate3x3(1000,1000) & 137.52 & 4.35 +- 0.01 & 3.91 +- 0.02 & - & 0.09 +- 0.00 & 0.48 +- 0.01\\
+sobel(1000,1000) & 104.02 & 0.49 $\pm$ 0.009 & 0.21 $\pm$ 0.004 & 0.60 $\pm$ 0.012 & 0.24 $\pm$ 0.018 & 0.17 $\pm$ 0.061\\
 \hline
-sobel(1000,1000) & 104.02 & 0.49 +- 0.00 & 0.21 +- 0.00 & 0.17 +- 0.03 & 0.24 +- 0.01 & 0.60 +- 0.01\\
+sqrt(float) & 14.99 & 1.37 $\pm$ 0.001 & 0.89 $\pm$ 0.000 & 1.06 $\pm$ 0.013 & 0.83 $\pm$ 0.010 & 0.85 $\pm$ 0.088\\
 \hline
-sqrt(Fix16) & 463.46 & 5.12 +- 0.00 & 2.96 +- 0.00 & 1.34 +- 0.03 & 1.14 +- 0.00 & 12.80 +- 0.04\\
+sqrt(int) & 13.91 & 3.22 $\pm$ 0.033 & 2.65 $\pm$ 0.001 & 1.06 $\pm$ 0.009 & 0.83 $\pm$ 0.014 & 1.25 $\pm$ 0.053\\
 \hline
-sqrt(float) & 14.99 & 1.37 +- 0.00 & 0.89 +- 0.00 & 0.85 +- 0.04 & 0.83 +- 0.01 & 1.06 +- 0.01\\
-\hline
-sqrt(int) & 13.91 & 3.22 +- 0.02 & 2.65 +- 0.00 & 1.25 +- 0.03 & 0.83 +- 0.01 & 1.06 +- 0.00\\
+sqrt(Fix16) & 463.46 & 5.12 $\pm$ 0.005 & 2.96 $\pm$ 0.007 & 12.80 $\pm$ 0.080 & 1.14 $\pm$ 0.009 & 1.34 $\pm$ 0.061\\
 \hline
 \end{tabular}
 }
 \end{center}
 \label{fig:benchmarks}
-\caption{Benchmark Results in Seconds. Arrays of length $10^5$ and
+\caption{Benchmark results in seconds with 95\% confidence intervals. Arrays of length $10^5$ and
   $10^6$ and matrices of size $1000\times 1000$ and $1000000 \times
   3$ are used. The one used in each benchmark is indicated in
   the leftmost column. For the matrices, only the number of rows are