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Slowly but surely an increasing number of MATLAB functions have grown to be able to take good thing about multi-core processors. For example, in MATLAB 2009b, functions like sort, bsxfun, filter and erf and the like gained to be able to spread the calculational load across several processor cores. This is very good news because in case your code uses strikes, in case you have a multi-core processor, then you'll get faster execution times without needing to modify your program. This kind of parallelism is termed implicit parallelism given it doesn t require any special commands so as to take benefit of multiple cores MATLAB just should it automagically. Faster code at no cost!
For lots of people though, this isn t enough and so they find themselves the need to use explicit parallelism where it gets the programmer s responsibility to see MATLAB precisely how to spread the task between the multiple processing cores available. The standard technique of doing this in MATLAB is with the Parallel Computing Toolbox but, unlike packages like Mathematica and Maple, this functionality will almost certainly cost you extra.
I ve been recently working with David Szotten, a postgraduate student at Manchester University, on writing parallel mex files for MATLAB using openmp and C. Granted, it s much less easy as utilizing the Parallel Computing Toolbox nonetheless it does work along with the results could be blisteringly fast since were working in C. It s also not quite as difficult even as we originally thought it will be.
Never, ever call many of the mex api functions within the portion of your code that you're trying to parallelise using openmp. Don t make an effort to interact with MATLAB whatsoever during the parallel percentage of your code.
The basis for the golden rule happens because, in the time writing no less than MATLAB 2009b, all mex api functions usually are not thread-safe this includes the printf function since printf is determined to be mexPrintf inside the mex.h header file. Stick to your golden rule therefore you ll be fine. Move away in the golden rule, however, therefore you ll find dragons. Trust me within this!
Let s get started with an example in order to find the sum of foox where foo is usually a moderately complicated function and x is really a large number of values. We have implemented a very example inside mex function mexsumopenmp.
I assume that you are running MATLAB 2009b on 32bit Ubuntu Linux version 9.04 much like me. If your setup differs from this then you can need to change the following instructions accordingly.
Before you begin. Close all currently running installments of MATLAB.
start MATLAB out of this terminal by entering matlab.
Inside MATLAB, proceed to the directory containing mexsumopenmp.c
Inside MATLAB, go into the following to compile the.c file
mex mexsumopenmp.c CFLAGSCFLAGS - fopenmp LDFLAGSLDFLAGS - fopenmp
Inside MATLAB, test the code by entering
xrand1, 9999999; %create a multitude of random x values mexsumopenmpx %calculate the sum fooxi
If all goes well, this calculation is going to be done in parallel and also you will be rewarded that has a single number. Time the calculation as follows.
My dual core laptop did this within 0.6 seconds normally. To see just how much faster it is compared to single core mode just repeat every one of the steps above but begin with export OMPNUMTHREADS1 You won t should recompile the mex function. On repeating this, my laptop made it happen in 1.17 seconds and therefore the 2 core mode is actually exactly twice faster.
That s virtually it the golden rule for doing things. I hope you find it useful and many thanks to David Szotten who forced me to be figure all this out. Comments welcomed.
just curious. Can Matlab 2008b do that job you aren't?
I can t understand why not although I haven t ever done it.
I m focusing on my thesis and try using OpenMP to parallelize a couple of things.
I am using MATLAB 7.1 and Visual Studio Pro 2008. With this certain things I ve done some small c-code and compile it successfully.
Now I m trying something more important and it not work.
Do you realize if you can call in the parallel for loop MATLAB functions?
mexCallMATLAB1, out, 3, input, CalcSomething;
About Help or ideas I d be very grateful.
I can easily see WHY you want to accomplish this since it will effectively be considered a replacement for MATLAB s parfor function you simply have to pay extra but it really is simply not possible currently.
I m afraid that what you are seeking to do only will not work since that you are calling a mex function in your parallelised loop. As I said inside article, this doesn't work since mex functions will not be thread-safe.
So, exactlty what can you do within your situation? Well, for the moment you do have a couple of options if you need to take the direct parallelisation route.
Remember, however, there's always a couple of way to break into a nut and, determined by your application, there could possibly be some other stuff you can use so that you can get your results more rapidly.
For example, had you been at my university then I might claim that you consider high throughput computing HTC on our experimental condor pool or replacing some key MATLAB functions with routines through the NAG Toolbox for MATLAB among other things.
The right off the bat I d suggest, however, should be to run your code with the MATLAB profiler and see if you're able to speed things up with good, long-established optimisation.
Thanks with this great article. Also wanted link towards the MIT toolbox. What are the attributes of using this functionality vs the MIT toolbox. When might you pick one within the other?
Also do I ought to install any openmpi in the ubuntu repositories?
Thanks with the insight. I ran the code about the cluster of my university. Just opening Matlab and running this software gave me no problems and I got what I expected. However, when I used nohup and OMPNUMTHREADS was bigger 1, everything was again executed correctly on multiple threads but I got the material Segmentation Fault from the end. Do you recognize why nohup caused that issue and do you know the consequences? Any response are going to be greatly appreciated.
January 13th, 2011 at 15:55
I have inked some tests on Matlab 7.9 under Linux and I get substantial time improvements based on the same MEX file compiled without OpenMP. However, when I compile exactly the same code in the machine with Matlab 7.5, I do not use whatever improvements.
Do you already know what could be the minimum version of Matlab that I should benefit from OpenMP in MEX files?
Or you think that it could certainly be a library problem, definitely not related on the Matlab version?
Thanks for the insights.
January 13th, 2011 at 16:13
Thanks on your comment. First off, I haven t ever done it in 7.5 so exactly what follows is guesswork
It ought to be the version of gcc that matters, not the version of MATLAB. So, what version of gcc have you been using for the MATLAB 7.5 machine? According to
you need gcc 4.2 or over to get OpenMP. That s where I would start to look to try and understand what you might be seeing.
Also, will you be definitely without doubt the MATLAB 7.5 machine carries a dual core processor?
January 13th, 2011 at 17:52
Thanks on your quick reply.
I am using gcc 4.4, you will find, your machine I am wanting to use has multiple cores. In fact I have been competent to succesfully compile and manage a stand alone program using openMP as machine; however, the MEX file will not seem to benefit on the MP capabilities. It looks like Matlab couldn t understand the value on the OMPNUMTHREADS variable.
Strange, because I am doing exactly precisely the same thing within machines.
February 13th, 2011 at 04:20
Thanks to the article, it got me started using openmp during my mex files.
The inability to utilize printf or mexPrintf inside parallel regions is really a problem. In case this software encounters errors, how do it warn an individual? One possible solution is to try using fprintf as well as print the big mistake messages with a file as opposed to to the screen. Is that feasible? Any knowledge about that, or for some other solutions?
I ve never used fprintf inside a parallel region so can t comment.
One approach that springs to mind is always to catch larger than fifteen within your parallel region and pass a mistake flag back on the main body in the mex function. You can then make use of this to inform a mexPrintf in the serial section from the code without causing any problems.
October 21st, 2011 at 12:36
I ve a similar problem as yours. Have you found any solution because of this?
November 15th, 2011 at 22:13
Many thanks just for this clearly explained example. I easily started using it working. I had to make use of gcc/4.2.4 with mex which wants gcc/4.2.3. Then I replaced gcc/4.2.4 with gcc/4.4.0 to have the shared OpenMP library. Here are the outcomes:
November 23rd, 2011 at 15:13
November 29th, 2011 at 19:44
Thanks for writing this. I managed to have it utilizing Matlab 2008b using Visual Studio Pro 2008 intel compiler through an almost 1-to-1 speed-up. A handful of things to remember when seeking to do this using a PC:
2 when compiling the mex file, the matlab command should is:
mex mexsumopenmp.c COMPFLAGSCOMPFLAGS - openmp LINKFALGSLINKFALGS - openmp
3 the Intel compiler also requires variables inside the c-code to become declared at the outset of routine. The following modified code worked to me.
February 23rd, 2012 at 21:54
This atricle is misleading. Your Golden Rule for the top on the article I agree with:
Never, ever call one of the mex api functions from the portion of your code that you'll be trying to parallelise using openmp. Dont make an effort to interact with MATLAB by any means during the parallel part of your code.
The a key point to notice is I do not have any parallel code inside mexFunction which can serve as MATLABs entry way into the C code. Likewise, I do not have any mex functions inside my spawnthreads function the part that's actually parallelised using openmp.
I don t begin to see the connection between Golden Rule and also the latter statements. Why can t you've OpenMP code as part from the mexFunction itself? As long as there won't be any API calls inside OpenMP part from the code it needs to work just great and does for me on the 32-bit WinXP machine with MSVC. I don t realize why you imply absolutely vital to have the OpenMP code in the separate function. Likewise, I don t discover why you suggest that there can t be any MATLAB API calls with your spawnthreads function. Should work fine and does personally as long as the API calls are certainly not inside any OpenMP code.
I wrote several mex functions using OpenMP. One in particular, MTIMESX, is about the TMW FEX section and it has been compiled on many different 32-bit 64-bit machines with some other Operating Systems by various users. The code has multiple installments of OpenMP and MATLAB API code within the identical function. It all appears to work fine. What I don t do is have API calls within the OpenMP code sections, your Golden Rule.
February 24th, 2012 at 01:38
Thanks to the comment. It s been some time since I wrote this informative article and I can t remember fondly the process I have to arrive at the proper execution that it is with all OpenMP loops within a separate function. I have hazy recollections of crashes when I tried putting an OpenMP loop within my main function but I honestly don t recall the details.
Looking in internet marketing with fresh eyes, I agree to you. There should be no reason I can t placed the OpenMP code inside main loop. So, I tested it I got eliminate spawnthreads and just squeeze OpenMP loop in the mexFunction plus it worked just great.
So either I was merely wrong or there were a issue with MATLAB 2009b on 32bit Ubuntu Linux version 9.04 that doesn t exist in this little current configuration 2011b on 64bit Ubuntu 11.04
Anyway, I ve removed the ultimate paragraph and believe that now this great article isn t misleading.
As this really is the best explanation I ve found about the web to getting openmp running inside a mex file, I ll just leave a several notes so others don t replicate my errors:
Firstly, some versions of matlab generally seems to reset OMPNUMTHREADS time for 1 on startup. You can check because of this by typing in matlab
Secondly, when your mex file is coded in C as an alternative to C, when compiling you have to change every type of CFLAGS from the above to CXXFLAGS. For example:
If you don t that will still compile and run perfectly, but nothing is going to be parallelised. Yeah yeah, I know, it must have been obvious, but I was convinced I d done something more fundamentally wrong and were overlook this all day
Other than this it runs great! Thanks for putting the following information online.
You are welcome Pat. Thanks for your extra tips:
January 14th, 2014 at 15:40
I wish to call some fortran subroutines such as openmp blocks from MATLAB. Is it possible to obtain parallel computation in fortran when it is known as by MATLAB? Is there a process for fortran just like C/C?
January 27th, 2014 at 18:09
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Simple dynamic analysis of your 1D bar confronted with axial body force, using Newmark time integration.
Simple dynamic analysis of an 1D bar afflicted by axial body force, using modal time integration.
The following files all solve 2D or 3D static linear elastic problems, but illustrate various refinements with the finite element method:
2Dplane strain/stress or 3D static linear elasticity code with fully integrated elements. The code is usually run using the following input files.
2D plane strain trouble with 4 noded quadrilateral elements;
2D plane strain downside to 8 noded quadrilateral elements;
Solves the beam bending problem discussed in Section 8.6.2, and compares the FEM solution with all the exact means to fix illustrate shear locking. This version with the code has to be run with solution with 4 noded quad elements.
Solves the beam bending problem discussed in Section 8.6.2 using incompatible mode elements, and compares the FEM solution with all the exact strategy to demonstrate the elements avoid shear locking. This version on the code has to be run with solution with 4 noded quad elements.
Solves the pressurized cylindrical cavity problem discussed in Section 8.6.2 with reduced integration elements, demonstrating hourglassing. This version on the code has to be run with solution with 4 noded quad elements.
Solves the pressurized cylindrical cavity problem discussed in Section 8.6.2 using selectively reduced integration, and compares the FEM solution while using exact solution. This version from the code have to be run with solution with 8 noded quadrilateral elements.
To download these files, right-click the web link and Save
FEM1DStatic.m Simple static analysis of any 1D bar put through axial body force
FEM1Dnewmark.m Simple dynamic analysis of an 1D bar exposed to axial body force, using Newmark time integration.
FEM1Dmodal.m Simple dynamic analysis of an 1D bar put through axial body force, using modal time integration.
The following files all solve 2D or 3D static linear elastic problems, but illustrate various refinements in the finite element method:
FEM2Dor3Dlinelaststandard.m 2Dplane strain/stress or 3D static linear elasticity code with fully integrated elements. The code might be run with all the following input files.
2D plane strain challenge with 4 noded quadrilateral elements;
2D plane strain issue with 8 noded quadrilateral elements;
FEMshearlockingdemo.m - Solves the beam bending problem discussed in Section 8.6.2, and compares the FEM solution while using exact strategy to illustrate shear locking. This version from the code need to be run with solution with 4 noded quad elements.
FEMincompatiblemodes.m - Solves the beam bending problem discussed in Section 8.6.2 using incompatible mode elements, and compares the FEM solution with all the exact answer to demonstrate that this elements avoid shear locking. This version with the code should be run with solution with 4 noded quad elements.
These are intended for advanced readers who're interested in developing their particular code. The code was created for educational purposes only - its full of bugs.
1. Basic utilities to learn and parse an ASCII input file. Four simple example input files are offered.
2. Linear, nonlinear and explicit dynamic solution procedures;
3. Routines you can save and solve both symmetric and unsymmetric FEA equation systems,
while using the conjugate gradient method or LU decomposition. A bandwidth minimizer is additionally included.
4. Subroutines containing basic element level operations - integration points, shape functions
and their derivatives for almost all standard 2D and 3D elements;
5. An implementation of fully integrated 3D linear elastic elements, which might be used being a template for
developing your own personal elements;
6. Routines to print the solution to some file that is usually read by TECPLOT tecplot focus is better.
1. Download the compressed folder
3. Compile the code. If you might be developing your individual code a visual development environment like PGI Visual Fortran or Intel Visual Fortran will continue to work best. The code will compile with free gfortran, however.
A developer project workspace is provided for Intel Visual Fortran from the directory FEACHEAP/IVFEACHEAP - you ought to be in a position to open these and pay attention to all the files. The main program is held in fecheap.f90.
A developer project workspace is provided for PGI Visual Fortran within the directory FEACHEAP/PVFEACHEAP - you ought to be competent to open this and discover all the files. The main program is held in feacheap.f90.
If you might be using gfortran, you need to first download and install gfortran the mingw build may seem to work fine with a Vista machine. To compile the code again, over a Windows environment, open
a console window, go for the directory called Code/src, and type buildfeacheap. This will compile the code that will create an executable file inside directory called Code/inputfiles. You can run the code in the directory Code/inputfiles by typing feacheap from the console window. Enter for your input file, and anything you like for that output file.
The input files could be edited using a standard text editor. There are four example input files:
calculates solution for just a 3D elastic plate which has a hole - 4000 nodes.
calculates dynamic solution for any 3d elastic plate which has a hole.
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Simple dynamic analysis of the 1D bar confronted with axial body force, using Newmark time integration.
Simple dynamic analysis of the 1D bar afflicted by axial body force, using modal time integration.
The following files all solve 2D or 3D static linear elastic problems, but illustrate various refinements in the finite element method:
2Dplane strain/stress or 3D static linear elasticity code with fully integrated elements. The code might be run while using following input files.
2D plane strain challenge with 4 noded quadrilateral elements;
2D plane strain downside to 8 noded quadrilateral elements;
Solves the beam bending problem discussed in Section 8.6.2, and compares the FEM solution together with the exact answer to illustrate shear locking. This version in the code have to be run with solution with 4 noded quad elements.
Solves the beam bending problem discussed in Section 8.6.2 using incompatible mode elements, and compares the FEM solution together with the exact strategy to demonstrate how the elements avoid shear locking. This version in the code need to be run with solution with 4 noded quad elements.
Solves the pressurized cylindrical cavity problem discussed in Section 8.6.2 with reduced integration elements, demonstrating hourglassing. This version on the code need to be run with solution with 4 noded quad elements.
Solves the pressurized cylindrical cavity problem discussed in Section 8.6.2 using selectively reduced integration, and compares the FEM solution together with the exact solution. This version in the code need to be run with solution with 8 noded quadrilateral elements.
FEM1DStatic.m Simple static analysis of an 1D bar put through axial body force
FEM1Dnewmark.m Simple dynamic analysis of any 1D bar afflicted by axial body force, using Newmark time integration.
FEM1Dmodal.m Simple dynamic analysis of your 1D bar afflicted by axial body force, using modal time integration.
The following files all solve 2D or 3D static linear elastic problems, but illustrate various refinements from the finite element method:
FEM2Dor3Dlinelaststandard.m 2Dplane strain/stress or 3D static linear elasticity code with fully integrated elements. The code might be run using the following input files.
2D plane strain challenge with 4 noded quadrilateral elements;
2D plane strain challenge with 8 noded quadrilateral elements;
FEMshearlockingdemo.m - Solves the beam bending problem discussed in Section 8.6.2, and compares the FEM solution with all the exact means to fix illustrate shear locking. This version on the code need to be run with solution with 4 noded quad elements.
FEMincompatiblemodes.m - Solves the beam bending problem discussed in Section 8.6.2 using incompatible mode elements, and compares the FEM solution with all the exact means to fix demonstrate that this elements avoid shear locking. This version on the code need to be run with solution with 4 noded quad elements.
These are intended for advanced readers whorrrre interested in developing their very own code. The code was created for educational purposes only - it includes bugs.
1. Basic utilities to see and parse an ASCII input file. Four simple example input files are given.
2. Linear, nonlinear and explicit dynamic solution procedures;
3. Routines to gather and solve both symmetric and unsymmetric FEA equation systems,
while using the conjugate gradient method or LU decomposition. A bandwidth minimizer is usually included.
4. Subroutines containing basic element level operations - integration points, shape functions
and their derivatives for the majority of standard 2D and 3D elements;
5. An implementation of fully integrated 3D linear elastic elements, which might be used being a template for
developing your own personal elements;
6. Routines to print the solution to some file that might be read by TECPLOT tecplot focus is most beneficial.
1. Download the compressed folder
3. Compile the code. If you happen to be developing your code a visual development environment including PGI Visual Fortran or Intel Visual Fortran works best. The code will compile with free gfortran, however.
A developer project workspace is provided for Intel Visual Fortran within the directory FEACHEAP/IVFEACHEAP - you need to be capable to open these and find out all the files. The main program is kept in fecheap.f90.
A developer project workspace is provided for PGI Visual Fortran inside directory FEACHEAP/PVFEACHEAP - you must be capable to open this and pay attention to all the files. The main program is held in feacheap.f90.
If that you are using gfortran, you ought to first download and install gfortran the mingw build may seem to work fine with a Vista machine. To compile the code again, on the Windows environment, open
a console window, go for the directory called Code/src, and type buildfeacheap. This will compile the code and build an executable file inside directory called Code/inputfiles. You can run the code from your directory Code/inputfiles by typing feacheap from the console window. Enter for your input file, and anything you like for your output file.
The input files is usually edited which has a standard text editor. There are four example input files:
calculates solution for any 3D elastic plate having a hole - 4000 nodes.
calculates dynamic solution for just a 3d elastic plate having a hole.
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