[R] Benchmarking R, why sort() is so slow?
Philippe Grosjean
phgrosje at ulb.ac.be
Fri Apr 27 14:03:47 CEST 2001
Hello everybody,
I am making a modified version of "Stephan Steinhaus' benchmark test for
number crunching, v. 2, (see
http://www.scinetificweb.com/ncrunch/ncrunch.pdf for the original version),
comparing several functions of some math/stat software. R is not performing
bad at all... except for the sorting of a 1,100,000 random vector (test #3)
which is the worst of all (see cell F3 in the following table). I simply
used the sort() function. Does anybody has an explanation for that?
Here is a temporary version of the results (sorry for the bad
presentation... a better one and code for all tests will be available soon):
Computer = Intel Celeron 500 Mhz with 256 Mb Ram under Windows 2000 sp1 (all
tests run 3 times)
Tested software: A = O-Matrix 5.1 (native mode), B = O-Matrix 5.1 (Matlab
mode), C = Matlab 6.0 R12, D = Ox 2.20, E = Mathematica 4.01, F = R 1.2.2, G
= Octave 2.1.31, H = Splus 2000 pro R3, I = Matlab 5.3 R11, J = Rlab 2.1, K
= Scilab 2.6, L = Axum 6.0 R2 (Splus mode)
Tests: 3 series (I. matrix calculation / II. preprogrammed matrix functions
/ III. programmation) of 5 tests each. A trimmed mean is considered for each
serie (best and worst scores eliminated). The tests are scaled to be done in
approx. 1 sec on the reference computer with the reference software being
Matlab 6.0 R12 (column C).
Serie I: Matrix calculation
1 = Creation, transposition, deformation of a 1200x1200 matrix, 2 =
1250x1250 normal distributed random matrix ^1000, 3 = Sorting of 1,100,000
random values, 4 = 550x550 cross-product matrix (b = a' * a), 5 = Linear
regression over a 700x700 matrix (b = a \ b')
Serie II: Preprogrammed matrix functions
6 = FFT over 900,000 random values, 7 = Eigenvalues of a 220x220 random
matrix, 8 = Determinant of a 750x750 random matrix, 9 = Cholesky
decomposition of a 1000x1000 matrix, 10 = Inverse of a 500x500 random matrix
Serie II: programmation
11 = 225.000 Fibonacci numbers calculation (vector calc.), 12 = Creation of
a 1500x1500 Hilbert matrix (matrix calc.), 13 = Grand common divisors of
35,000 pairs (recursion), 14 = Creation of a 220x220 Toeplitz matrix
(loops), 15 = Escoufier's method on a 22x22 matrix (mixed)
Times are in sec.
Software A B C D E F(R) G H I J K
L
Serie I.
--------
1 1.03 1.35 0.95 1.23 2.19 2.79 4.24 2.91 1.00 1.92 2.95
2.67
2 2.60 2.48 0.99 2.91 0.76 2.88 2.76 1.41 2.61 2.38 2.32
1.42
3 1.09 1.10 1.04 3.37 3.13 21.71 6.33 1.84 7.91 4.00 2.72
1.65
4 0.85 0.86 0.98 1.84 4.04 3.88 7.71 1.74 4.57 8.92 14.84
1.61
5 0.72 0.96 1.07 5.47 2.89 5.75 4.32 10.84 6.91 6.31 7.67
9.70
----------------------------------------------------------------------------
----
tr. mean 0.99 1.14 1.01 2.70 2.74 4.17 4.96 2.16 4.70 4.23 4.45
1.98
Serie II.
---------
6 2.21 2.16 0.99 3.83 1.94 2.67 2.70 4.81 4.04 2.23 4.06
4.17
7 0.42 0.42 0.90 1.11 0.95 1.49 2.46 1.68 1.41 3.09 2.94
2.19
8 0.88 0.92 1.06 3.31 5.90 5.98 5.19 2.04 7.96 7.50 9.14
117.0
9 0.96 0.96 1.29 3.74 4.30 3.65 6.54 11.38 4.59 6.64 7.81
8.98
10 0.75 0.76 1.10 2.94 5.24 5.83 3.92 2.48 6.18 7.65 7.61
11.04
----------------------------------------------------------------------------
----
tr.mean 0.75 0.88 1.05 3.33 3.83 4.05 3.94 3.48 4.93 5.74 6.49
8.06
Serie III.
----------
11 0.52 0.39 1.04 0.52 0.41 0.55 1.02 0.96 0.97 0.49 0.60
0.72
12 1.79 2.09 0.98 1.03 3.14 1.30 0.79 2.02 2.03 1.21 1.92
1.86
13 0.38 0.43 1.03 0.76 ? 1.33 0.87 0.71 0.92 6.03 1.23
0.93
14 0.20 0.26 0.92 0.13 1.50 2.56 6.89 14.49 0.78 0.63 4.52
13.46
15 0.22 0.24 1.00 0.13 ? 0.48 1.66 12.12 1.06 0.73 1.32
10.88
----------------------------------------------------------------------------
----
tr. mean 0.79 0.36 1.00 0.47 1.68 1.06 1.19 5.03 0.99 0.86 1.49
4.56
----------------------------------------------------------------------------
----
total 14.62 15.39 15.34 32.33 36.38 62.84 57.40 71.43 52.95 59.73 71.65
188.3
----------------------------------------------------------------------------
----
ovr. mean 0.84 0.79 1.02 2.17 2.75 3.09 3.36 3.44 3.54 3.61 4.14
4.87
Best Regards,
Philippe Grosjean
...........]<(({°<...............<°}))><...............................
) ) ) ) ) __ __
( ( ( ( ( |__) | _
) ) ) ) ) | hilippe |__)rosjean
( ( ( ( ( Marine Biol. Lab., ULB, Belgium
) ) ) ) ) __
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