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Dear all,
I have a chance to test the latest MKL 11.0 in Vindows 7 64bit + VS2010 + Intel Composer XE 2013, I need to call the PARDISO, to my supprise, the link libraies setting in my project can't be found, I have to link the mkl_core.lib, after several miniutes waiting, I got the static linked library, it reaches to more than 300 MB, So terrible! Why not let the program only link the needed objects? or I don't have a correct project setting?
In addition, it seems that with the latest MKL 11.0 + Intel Composer XE 2013, the generated executable file is much slower than MKL 10.x + Intel Composer XE 2012, what's wrong?
Thanks,
Zhanghong Tang
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I also found that MKL 11.0 with ivf2013is much slower than MKL 10.x with ivf2011.
I used it to compiler FORTRAN MEX files on win8 with MATLAB 2012b x64 with the following option setting:
set COMPILER=ifort
set COMPFLAGS=/fpp /Qprec /I"%MATLAB%/extern/include" /c /nologo /free /fp:source /MT /assume:bscc
set OPTIMFLAGS=/O3 /DNDEBUG /QxHost /Qvec-report /Qftz
set DEBUGFLAGS=/Z7
set NAME_OBJECT=/Fo
In the MEX file, only subroution MATMUL is used.
For mkl 10.x with ivf2011, the excution time is ~348 s while for mkl 11.0 with ivf2013, the excution time is ~518 s.
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Besides, my laptop has a i3-M330 CPU with 8Gbit memory.
Both visions are tested on the same laptop and compiled with exactaly the same options.
I've also tested the 32bit version, it's slower than the 64bit version, and ivf2013 still much slower than ivf2011.
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junziyang wrote:
I also found that MKL 11.0 with ivf2013is much slower than MKL 10.x with ivf2011.
I used it to compiler FORTRAN MEX files on win8 with MATLAB 2012b x64 with the following option setting:
How we can check it on our side? can we get us the C/C++ or F77/F90 examples to check?
what is the problem size? routines? CPU type?
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Sorry. It's a FORTRAN 90 MEX file in a MATLAB project. So it's imposible to run and test it seperately.
The matrix size is about 200x100x1000000. A matrix of 200x100 is used in the calculation and then store the results into sequential pages of a 200x100x1000000 matrix and then store it to the harddisk.
The SAME program is compiled with the SAME options with different version of compiler and tested with the SAME parameters.
Most of the computation time is involved with MATMUL.
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Ok, is that double precision or complex double or another one?
and i see you used Intel Core i3 Processor. Did you link with threaded or sequentional libs: mkl_sequential.lib mkl_intel_thread.lib ?
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Yes. I put all the *.lib on the LINKFLAGS path.
It's double precison.
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I’ve tried s-,d-, and cgemm performance on Core i7 machine with RHEL Server 6.3 64-bit.
Sizes used in calculations: 200x100x1000000
Didn’t see performance degradation from 10.3 to 11.0. Got the following results
sgemm:
2 threads, 10.3: 0,371785 sec
2 threads, 11.0: 0,384604 sec
4 threads, 10.3: 0,198118 sec
4 threads, 11.0: 0,205439 sec
dgemm:
2 threads, 10.3: 0,747748 sec
2 threads, 11.0: 0,751148 sec
4 threads, 10.3: 0,395847 sec
4 threads, 11.0: 0,400287 sec
cgemm:
2 threads, 10.3: 1,56011 sec
2 threads, 11.0: 1,55974 sec
4 threads, 10.3: 0,885265 sec
4 threads, 11.0: 0,88249 sec


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