[R] comparing execition time: R vs matlab linear algebra...
Rick Reeves
reeves at nceas.ucsb.edu
Wed Apr 19 23:00:18 CEST 2006
Greetings:
We are evaluating the performance of R matrix algebra es as we port a
MATLAB R14 script into R.
The MATLAB code basically evaluates the AX=B system on sparse matrices
that result in output matrices of 100 to 1,000,000 rows/columns.
Our R prototype script uses spase base matrices and the methods qr() and
qr.coeff().
The following statements are called inside a doubly-nested loop:
G is an n x m sparse matrix with most nonzero values near the main diagonal,
CURR is an n x 1 vector:
the MATALAB script:
P = G \ CURR; % matrix left-divide
QG = qr(G)
P = qr.coef(QG, CURR)
The answer we get matches that of the MATLAB code for small (60x60)
problems.
But, execution times are much longer (40 minutes, compared to 2 minutes for
the MATLAB script)
Also, the R version cannot accommodate a solution matrix greater than
aprox 10,000 x 10,000 elements,while the MATLAB script has generated
solutions for 10**6 x 10**6 solution matrices.
My questions: 1) Have others noticed this difference in performance
between R and MATLAB?
2) Is there R literature (I have searched, not
found) that discusses optimizing these
solutions in R? such as.....
2) Would developing a solution using the Matrix or
SparseM classes improve performance?
Thanks in advance for any insights!
Regards,
Rick R
--
Rick Reeves
Scientific ProgrammerAnalyst
National Center for Ecological Analysis and Synthesis (NCEAS)
University of California, Santa Barbara
reeves at nceas.ucsb.edu
805 892 2533
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