How can I compile an octave program in order to sell it? The only octave compilers I've seen are for C programs with some of the octave functions. My objective is to obfuscate the code and ease exportability.
To answer "How can I compile an octave program in order to sell it?". There is no equivalent to the MATLAB Compiler in octave. To deploy code you can deploy octave for free bundled with your m code, which eliminates most of the use cases for the MATLAB Compiler.
This leaves the obfuscation, here the Matlab Compiler basically uses pcode. There is no such thing in octave which can obfuscate and run m code for you.
To answer the wider question of protecting intellectual property. There is the potential to obfuscate your Octave M code by removing comments, replacing variable names and so on, but to my best knowledge such a thing does not exist.
This only leaves oct file, which reading your question you probably don't consider an option. Oct files can only be created form C, C++ and Fortan code, not from Octave M code.
Please not that this is a purely technical answer and does not consider potential legal limitations imposed by GPL.
Related
Suppose I write a program in both Python and C++ and I turn these to executable. Now, will both the executable have the same speed or will it vary (I guess it shouldn't cause it should now be in machine code form) ?
Suppose I write a program in both Python and C++ and I turn these to executable. Now, will both the executable have the same speed
Of course usually not (assuming both code implement the same algorithm). And the runtime speed depends a lot of the compiler itself (e.g. tinycc -for C- and GCC or Clang ....) and even of its versions and compilation flags (e.g. -Os vs -O2 with g++). BTW, Python is compiled to some bytecode, not to machine code.
Of course, some software are mostly spending CPU time elsewhere (e.g. in some relational database manager such as PostGreSQL). Then rewriting them in C++ instead of Python won't gain a lot of performance. And some software are mostly IO bound (e.g. tar(1) used without compression)
At last, some C++ programs could generate machine code at runtime (e.g. using AsmJit...) using partial evaluation techniques, which may give a huge speedup.
On Linux, you could generate some C or C++ code at runtime, compile it as a temporary plugin, then dlopen(3) that temporary plugin (fetching new function pointers using dlsym(3)... Adapt the manydl.c example to your needs)
Also, C++ is a very difficult language to learn. Read some good book about it.
Read of course the Dragon book.
Since an entire book is needed to answer your question !
I have to prove to my client that Fortran is faster than Matlab/Simulink. He is considering migrating a code from fortran to Matlab. The code is mainly logic and "procedural" subroutines. It does not use any native matrix operations or mathematical functions (eigenvalues, non linear equations, etc)
I think that the question of who is faster is already answered considering several references over the internet and the "intrinsic characteristics" of each language, but I need concrete data.
All charts that I found compare Matlab/Simulink x Fortran but do not specify if the Matlab code is compiled or not (using matlab coder toolbox). I think that it is a critical issue.
I´m not saying that compiling the code will make matlab faster than fortran, but in order to really convince someone I would like to see the results.
A good start would be:
Performance - Matlab (.m) compiled (Matlab coder toolbox) X Intel Fortran
Performance - Simulink compiled (Realtime toolbox) X Intel Fortran
Does anyone have already tested this scenario?
Matlab code that I recently "compiled" using the Matlab Coder produced a speed-up of x20 (!). The actual expected speedup depends on many things. If your Matlab code is highly vectorized and uses mainly linear-algebra routines, then the Coder is unlikely to produce much speedup. But if you have multiple loops and conditionals in your algorithm then you can indeed achieve order-of-magnitude speedup as in my example above.
Under the hood, Matlab's linear-algebra uses BLAS/LAPACK (via the MKL/ACML libraries), that use highly-optimized Fortran code. So unless you write extremely efficient Fortran, it is not likely that you will be able to outperform Matlab (despite the function-call overheads) for highly-vectorized Matlab linear-algebra/math algos. However, if your code uses conditionals/loops and similar non-math programming constructs, then the picture might change. In short, there's no simple answer - it depends on your specific algorithm/program.
Putting performance aside for the moment, Matlab has numerous other benefits over Fortran, including a vast array of tested built-in functions and enabling a rapid development cycle.
You would need to ask a more tightly defined question - there's no single answer to whether Fortran is faster than MATLAB/Simulink.
First of all, it's easy to write terrible, slow algorithms in either language. So you'd need to specify particular, well-written algorithms.
Secondly, there are many things for which MATLAB will be faster than even very well-written Fortran (or C). For example, if you want to multiply two big matrices together, or calculate some eigenvalues, or other linear algebra that is in MATLAB's sweet spot, you won't beat it. On the other hand if you're doing something with a lot more logic, that can't be vectorised, Fortran is likely to be faster (as long as it's written well).
When you introduce MATLAB Coder into the picture, these latter things are the ones that are most likely to benefit from a speedup by converting to C code (mostly because the former things really can't be sped up much, which is why you wouldn't beat them). But the speedup is variable - I've seen over 10-15x, but also sometimes only 1-2x.
You don't mention where you found the charts you have comparing MATLAB to Fortran, but if you've found them on the internet I would think it's a pretty safe assumption that they don't involve C code generation with MATLAB Coder, and represent the performance of just MATLAB.
Finally - one other method of speeding up MATLAB is to parallelize it with Parallel Computing Toolbox (which enables you to parallelize things over the cores on your local machine) and possibly also with Distributed Computing Server (parallelization on cluster). It's typically a lot easier to do this with MATLAB code than it is to speed up by using MATLAB Coder to produce C code - so if you think it's critical to consider MATLAB Coder in your comparisons, you should probably also consider this as well.
MATLAB Compiler will not make your code faster, it is intended for distributing your code to third party users that do not have MATLAB. You need to provide, along with your compiled code, the MCR or MATLAB Component Runtime, which is essentially a headless version of MATLAB, and which you can distribute freely if you have a license of MATLAB Compiler.
Now, if you use MATLAB Coder (or Simulink Coder for Simulink) to generate C code from your MATLAB code, then it is likely that you will get a speed up compared to interpreted MATLAB code. Even then, that depends on the code in question. Also, this only supports a subset of the MATLAB language, that is compatible with C code generation.
As great as MATLAB is as a mathematical language, its speed is not as fast as one like it to be. I am wondering what are the general practices to speed up running a MATLAB code? For example I know that if instead of running for loops one can do computations in vector/matrix format s/he will see speedup in running the code.
I am wondering what are other suggestions.
Here are a few basic performance tips:
Learn to use the profiler to understand which parts of your
computation are slow
Limit the amounts of expensive function calls via vectorization
Preassign arrays instead of growing them in loops
Use multithreaded functions (such as bsxfun)
Use the latest version of Matlab - there have
been tremendous performance gains over the last 5 years
Use the parallel toolbox for multicore and/or GPU processing
Use efficient algorithms
Use Java or C/C++ code where appropriate (though the speed-up can be disappointing)
If you're doing a lot of easily-parallelizable operations, parfor will automatically parallelize your for loops: http://www.mathworks.com/help/toolbox/distcomp/parfor.html
Installing Lightspeed.
I have recently been through the frustrating process of installing Tom Minka's Lightspeed on my Mac. Along the way I learnt a few hard lessons worth sharing with other Mac users.
My system has the following specifications
OS X version 10.8.5
Xcode version 4.6.3
Matlab version 2011a
1) Make sure that Lightspeed is installed on a path with NO spaces in its name. I made the mistake of putting it inside "Library/Application Support/Matlab" which caused me endless trouble. In particular, it led to the same issue reported by Tomer Levinboim (levinboim.blogspot.co.nz) with the added problem that his fixes did not fully resolve the problem!
2) Read Michel Valstar's notes "Compiling Matlab Mex Files on a Mac" and install the recommended patch from Mathworks ( http://www.mathworks.com/matlabcentral/answers/94092). This patch applies all the needed flag/option changes that Levinboim identifies.
3) Change the line options.COMPFLAGS in the install_lightspeed.m file inside the lightspeed folder to point to:
/Applications/Xcode.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX10.8.sdk
4) In Matlab check that the current path points at the Lightspeed folder. Run the command install_lightspeed. If successful run test_lightspeed. You should now have a working version of Lightspeed!
5) Path settings persist between sessions so the startup.sh approach suggested in the Read Me appears to be unnecessary on a Mac. However, if you wish to go down that track, first read:
Where is startup.m supposed to be?
http://obasic.net/set-your-customized-startup-file-for-matlab .
You might begin reviewing some ways to begin thinking about vectorization here.
After that, the PDF given here, even though incomplete, provides many Matlab idioms that give good performance.
I just found this here: Writing Fast MATLAB Code.
by Pascal Getreuer and this here: Lightspeed Toolbox. Great stuff...
I have to exploit PpenMP in some algorithm and for this purpose I need some mathematical functions, like eig or svd as it is available in MATLAB and it is quite fast in MATLAB. I already tried the following libraries with OpenMP
GSL - GNU Scientific Library
Eigen C++ template library
but I don't know why my OpenMP parallelised code is much slower than the serial code, may be there is some thing wrong in the library, or that the function random, eig or svd are blocking? I have no idea how to figure it out, can some body suggest me which is most compatible math library with OpenMP.
I can recommend Intel's MKL; note that it costs money which may affect your decision. I neither know nor care what language(s) it is written in, just so long as it provides APIs callable from my chosen language. Mine is Fortran, but it has bindings for C too
If you look around SO you'll find many questions from people whose first (or second or third) OpenMP programs were actually slower than their serial versions. Look at some of the answers. Don't conclude that there is a magic bullet, in the shape of a library, to make your code faster. Instead, realise that it is most likely that you've written a poorly-parallelised program and fix that.
Finally, if you have an installation of Matlab, don't expect to be able to write your own routines to outperform Matlab's. I won't say it can't be done, but I think you'll find it very difficult.
GSL is compatible with OpenMP. You can try with Intel Math Kernel Library which comes as a trial version for free.
If the speed up is not so much, then probably the code is not much parallelizable. You may want to debug and see the details of the running threads in Intel Thread Checker, that could be helpful to see where the bottlenecks are.
I think you just want to find a fast implementation of lapack (or related routines) which is already threaded, but it's a little hard to tell from your question. High Performance Mark suggests MKL, which is an excellent example; others include ATLAS or FLAME which are open source but take some doing to build.
Here is a good linear solver named GotoBLAS. It is available for download and runs on most computing platforms. My question is, is there an easy way to link this solver with the Mathematica kernel, so that we can call it like LinearSolve? One thing most of you may agree on for sure is that if we have a very large Linear system then we better get it solved by some industry standard Linear solver. The inbuilt solver is not meant for really large problems.
Now that Mathematica 8 has come up with better compilation and library link capabilities we can expect to use some of those solvers from within Mathematica. The question is does that require little tuning of the source code, or you need to be an advanced wizard to do it. Here in this forum we may start linking some excellent open source programs like GotoBLAS with Mathematica and exchange our views. Less experienced people can get some insight from the pro users and at the end we get a much stronger Mathematica. It will be an open project for the ever increasing Mathematica community and a platform where these newly introduced capabilities of Mathematica 8 could be transparently documented for future users.
I hope some of you here will give solid ideas on how we can get GotoBLAS running from within Mathematica. As the newer compilation and library link capabilities are usually not very well documented, they are not used by the common users very often. This question can act as a toy example to document these new capabilities of Mathematica. Help in this direction by the experienced forum members will really lift the motivation of new users like me as well as it will teach us a very useful thing to extend Mathematica's number crunching arsenal.
The short answer, I think, is that this is not something you really want to do.
GotoBLAS, as I understand it, is a specific implementation of BLAS, which stands for Basic Linear Algebra Subroutines. "Basic" really means quite basic here - multiply a matrix times a vector, for example. Thus, BLAS is not a solver that a function like LinearSolve would call. LinearSolve would (depending on the exact form of the arguments) call a LAPACK command, which is a higher level package built on top of BLAS. Thus, to really link GotoBLAS (or any BLAS) into Mathematica, one would really need to recompile the whole kernel.
Of course, one could write a C/Fortran program that was compiled against GotoBLAS and then link that into Mathematica. The resulting program would only use GotoBLAS when running whatever specific commands you've linked into Mathematica, however, which rather misses the whole point of BLAS.
The Wolfram Kernel (Mathematica) is already linked to the highly-optimized Intel Math Kernel Library, and is distributed with Mathematica. The MKL is multithreaded and vectorized, so I'm not sure what GotoBLAS would improve upon.