I have built an standalone Matlab application. I was expecting it to be faster than running the application from the Matlab environent but it is indeed a bit slower (1.3 seg per iteration vs 1.5 seg per iteration)
I am not counting the init time required by MCR but the execution of my code.
Is that the expected performance or should I be obtaining a performance improvement?
I haven't found any settings on the deployment tool that could help to reduce execution time.
Thanks in advance
Applications built with MATLAB Compiler should execute at pretty much exactly the same speed as within MATLAB.
MATLAB Compiler does not convert your MATLAB code into machine code in the same way as a C compiler does for C. What it does is to archive and encrypt your MATLAB code (note, it properly encrypts it, not just pcodes it as a comment suggests), create a thin executable wrapper and package them together, possibly also with MATLAB Compiler Runtime (MCR). MCR is very similar to MATLAB itself, without a graphical user interface, and is freely redistibutable.
When you run the executable, it dearchives and decrypts your MATLAB code and runs it against the MCR. It should run exactly the same, both in terms of results and speed.
Very old versions of MATLAB Compiler (pre-version 4.0) worked in a different way, converting a subset of the MATLAB language into C code, and compiling this. This provided a potentially significant speed-up, but only a subset of the language was supported and results, unless you were careful, could sometimes be different. Similar functionality is now available in the separate MATLAB Coder product.
There are a few small things you can do to improve performance: for example, within deploytool you can specify which toolboxes your application uses. deploytool uses a dependency checker to package up all MATLAB functionality that it thinks your code might possibly depend on, but it can't always tell exactly, as the functions your code needs might change at runtime. It therefore errs on the side of caution and includes more than necessary. By specifying only the toolboxes you know to be necessary, you can speed things up a little (it also speeds up the build process quite a bit).
Related
I am struggling with MATLAB installation. At the last step, it shows the following message:
However, I have the .net and gcc compilers on my system. It is odd that it also requires MATLAB compiler. Because it should be installed with the software itself automatically. I have never faced such problems when installing earlier versions of MATLAB.
You don't face a problem at all. The installation runs smoothly and MATLAB will work perfectly. Only if you want to translate MATLAB code to C code with the Matlab Coder toolbox, you require a C compiler (such as gcc or MinGW). This is obvious. Why should MATLAB ship a C compiler if there are plenty of open-source compilers (of which you maybe have a preference?) and there is no need for MATLAB (the main product) to run it?
With Simulink it is a bit less obvious but the argument is the same (why should MATLAB ship a C compiler?). Simulink is a model-simulation tool, calling a once-defined model over and over again at fixed (or dynamic) time steps. To speed up the calculation, it actually compiles the code first (note that MATLAB mainly uses a just-in-time-compiler). Those are .mex files, which is basically compiled C code (similar to a DLL). For this, it needs a C compiler and which brings us again back to the argument of why to ship one.
To make a long story short: Don't worry. Go ahead, this is not a problem and totally normal. As you said, you already have a C compiler installed (gcc), MATLAB will find it and you will probably not even note it if you start Simulink or the MATLAB Coder.
BTW, MATLAB always had required those compilers. Only the Java runtime is shipped with it since R2013-something because they had a major issue with the Java update back then and the tech support went nuts =P
As you may know, extrinsic functions are not outputted during the code generation process. Are there alternatives to these functions and/or solutions to this problem? My code generation error report is shown below :
Code Generation Error Report
I am surprised that I can't output size and rgb2gray either. Since these are essential to my program, I cannot avoid them.
Help will be much appreciated!
This is a good question, and I see similar questions fairly frequently. As I started using MATLAB Coder, one of the biggest pitfalls was the constant search for supported functions. I sympathize with your frustration, and I have a few tips, having been through this.
First, to your direct question, while imread isn't supported by Coder, size and rgb2gray are. Probably Coder is complaining about these because they have been passed mxArrays from the call to imread, which is fine when it is extrinsic, but not ok for separate generation. That's just a guess. A very useful tool in writing code is the list of Coder supported functions: List of Functions supported in MATLAB Coder
But even with those two, to replace imread is not a tiny task. You'll have to find another library that supports the particular file you're working with, and then stitch that in using coder.ceval. Alternatively, if you can find a pure MATLAB implementation of it, that might help.
Are you targeting a pure C library or a MEX file? If you intend to use this code within the MATLAB environment, you can always use imread separately and then pass the data.
And now to some more general observations: MATLAB Coder isn't a perfect MATLAB to C translation system. It's extremely powerful, and I've been able to write some very large projects with it, but if what you want is the ability to run any MATLAB code without MATLAB around, you should look at MATLAB Compiler, a different add-on. There's a very good Q and A about this here: MATLAB Compiler vs MATLAB Coder
When writing projects in MATLAB Coder, it's really best to start from scratch, knowing you're targeting C code ultimately. There are so many gotchas in the conversion from MATLAB to C that you have to be always vigilant while writing the MATLAB code.
One tool that helps is to right-click on a file in the "Current Folder" list that usually resides on the left-side of the main window, and select "Check Code Generation Readiness." You'll get a great report of potential problems in the file. I recommend using this often.
Another useful tool is to always put the %#codegen tag into your code. This alerts the MATLAB editor that the .m file is intended for code generation, so it provides extra context-sensitive information while you're writing the file. This helps enormously.
The most commonly missing functions for code generation are file IO functions. There are some good reasons for this, but it's frustrating nonetheless.
When you stitch in external C code, you use the coder.ceval function, which can provide excellent access to external libraries. Using this well is a whole other topic, outside the scope of this question.
If you can indicate exactly what kind of files you're interested in reading (PNG, BMP, TIFF, etc.) perhaps someone may be able to identify a good external library for you to use.
How can I make the Matlab Compiler run faster?
It takes ages for it to make a sensible program.
I am currently using
Microsoft Visual C++ 2008 SP1 in c:\Program Files (x86)\Microsoft Visual Studio 9.0
in mbuild -setup
Here's one tip - a large part of the time taken by MATLAB Compiler is in dependency checking for the code you're compiling. If you have a large number of toolboxes on the path, this can take a long time. You can change the list of toolboxes on the path from the Settings menu in deploytool. Make sure that only the toolboxes your code actually uses are selected.
It may still take ages, but this can make it take slightly less ages.
Usual techniques for making anything compile faster as usually pretty cost-effective:
Faster CPU
More memory
SSD
I don't think that the parallel computing toolbox will help you because the MATLAB compiler isn't listed as having built-in support on the website even though the coder products do.
Matlab Coder is a recently released MathWorks product. My understanding is that it is a Matlab-to-C compiler with the biggest advantage over previous solutions being that the resulting program does not need to be linked against a Matlab shared library.
Can someone with access to this product confirm the above? What are the dependencies of the translated programs and what kind of performance are we talking about? Also I would really like to see some example outputs, to know if the resulting C programs can be understood and improved without access to the Matlab source.
If done right this could be very powerful, allowing rapid prototyping in Matlab and instantaneous conversion to C when things are getting serious. I kind of whish it doesn't work well so that Python+Numpy+Scipy.weave is still superior ^^.
MATLAB Coder can allocate memory using malloc, so you can generate C code from MATLAB functions that operate on dynamically sized data. You can also choose the option of static allocation with a maximum size for variables.
RE: using BLAS for matrix multiplication – while the generated C code doesn’t automatically include any processor/platform specific optimizations, there is a feature called Target Function Library, which that allows users to write their own implementation of primitive operations (such as matrix multiplication), and include them in the generated code. You can hook up BLAS libraries to MATLAB Coder via that method. There’s also an ability to include optimized processor specific calls for larger functions through custom code integration and conditional compilation that lets you specify one set of code to use for code generation, and another set for simulation (for example, an optimized FIR function for an Texas Instruments DSP and functionally equivalent code for simulation that can execute on your PC written in C or in MATLAB).
Hope this is helpful --
Arvind Ananthan; Product Manager for MATLAB Coder; MathWorks
I am using Matlab Coder with Real Time Workshop (RTW) in order to generate self standind standard C code.
First of all you are asked to use a Matlab subset called "Embedded Matlab" you can find the doc about it on the web
You also have to avoid any dynamic exploitation of variables and you can't obviously generate c code for plots or figures.
The code it generates could be a mess to understand but it works.
you should actually not try to understand it. In a certain way it is as you would try to understand the assembler your compiler generates from a C code you wrote, quite pointless.
another thing you should take care of is to declare persistent big data types (vectors, big arryes, etc.) otherwise they will be allocated into your stack...
good luck!
I have a project that requires lots of image processing and wanted to add GPU support to speed things up.
I was wondering if i compiled my matlab into c++ shared library and called it from within OpenCL program, does that mean that the matlab code is going to be run on GPU?
My own (semi-educated) guess is that you are going to find this very difficult to do. But, others have trodden the same path. This paper might be a good place to start your research. And Googling turned up Accelereyes and a couple of references to items on the Mathworks File Exchange which you might want to follow up.
Everything in jacket is written in c/ c++ / cuda.
Infact we now have a beta version libjacket (http://www.accelereyes.com/downloadLibjacket) which can be used to extend not just matlab but other languages if you are willing.
#OSaad
Most of our functions are the fastest options out there. Be it in C or matlab.
The Parallel Computing Toolbox in the upcoming release R2010b (due September 1st) supports GPU processing for several functions. Unfortunately, it only supports CUDA (version 1.3 and later), so with an ATI graphics card, you're out of luck. However, you may just want to buy a dedicated GPU, anyway.
Typically, if you can write your Matlab code in a "vectorized" way, then the packages like AccelerEyes and Jacket have a reasonable chance of making things run on the GPU. You can verify this to some extent beforehand by checking whether Matlab itself is able to run on multiple cores on the CPU (these days Matlab will use multiple cores if things are parallelizable in an obvious way).
If that doesn't work, then you need to drop down to C/C++ via mex and then, from there, call OpenCL yourself. Mex is how Matlab talks to C code, so you write C code that is called by Matlab (and receives the matrices, etc), then initialises and calls OpenCL. This is more work, but may be your only route (and, even if the automated packages work to some extent, this approach can still give more speedups because you can be smarter about memory management, for example, if you know what your are doing).