I want to run a simulation which includes SimEvent blocks (thus only Normal option is available for sim run) for a large number of times, like at least 1000. When I use sim it compiles the program every time and I wonder if there is any other solution which just run the simulation repeatedly in a faster way. I disabled Rebuild option from Configuration Parameter and it does make it faster but still takes ages to run for around 100 times.
And single simulation time is not long at all.
Thank you!
It's difficult to say why the model compiles every time without actually seeing the model and what's inside it. However, the Parallel Computing Toolbox provides you with the ability to distribute the iterations of your model across several cores, or even several machines (with the MATLAB Distributed Computing Server). See Run Parallel Simulations in the documentation for more details.
Related
I'am trying to run real-life experiment for an application in Raspberry Pi, and I need to estimate or predicate the execution time for the application. in other words, before execution/ run the task i need to know how long (roughly) this task/app going to take to get the result back. I have identified several techniques and works that have been done before. but most of it are simulation work which doesn't work with real-life experiment. does anyone can help me with any idea or technique (No code). thank you in advance
Estimating the execution time of an application or function is going to be difficult in any context. You might want to look up the halting problem for some insight to why. It's impossible to determine whether a given program will finish executing, and therefore, you can't really tell how long a given program will take to finish executing.
For general computing, varying hardware capabilities of any given system will always have an effect on the execution time of a program. Raspberry Pi is a little more discrete than that, and therefore more predictable in that sense, but those specifications will not always be consistent across its various versions. That adds to the complexity of determining a run time.
Practically, the most reliable way to determine how long a process will take would be to just run it and time it. If you absolutely need predicted times for something, you might be able to do a bit of a composite estimate - time the smaller chunks of the application separately, and then use those to determine how long you expect the application as a whole to run. For most situations, though, it would be much faster to just run the program itself rather than trying to predict it.
Store the time before and after the execution ? Then you could know the execution time
I'm working on an adaptive and Fully automatic segmentation algorithm under varying light condition , the core of this algorithm uses Particle swarm Optimization(PSO) to tune the fuzzy system and believe me it's very time consuming :| for only 5 particles and 100 iterations I have to wait 2 to 3 hours ! and it's just processing one image from my data set containing over 100 photos !
I'm using matlab R2013 ,with a intel coer i7-2670Qm # 2.2GHz //8.00GB RAM//64-bit operating system
the problem is : when starting the program it uses only 12%-16% of my CPU and only one core is working !!
I've searched a lot and came into matlabpool so I added this line to my code :
matlabpool open 8
now when I start the program the task manger shows 98% CPU usage, but it's just for a few seconds ! after that it came back to 12-13% CPU usage :|
Do you have any idea how can I get this code run faster ?!
12 Percent sounds like Matlab is using only one Thread/Core and this one with with full load, which is normal.
matlabpool open 8 is not enough, this simply opens workers. You have to use commands like parfor, to assign work to them.
Further to Daniel's suggestion, ideally to apply PARFOR you'd find a time-consuming FOR loop in you algorithm where the iterations are independent and convert that to PARFOR. Generally, PARFOR works best when applied at the outermost level possible. It's also definitely worth using the MATLAB profiler to help you optimise your serial code before you start adding parallelism.
With my own simulations I find that I cannot recode them using Parfor, the for loops I have are too intertwined to take advantage of multiple cores.
HOWEVER:
You can open a second (and third, and fourth etc) instance of Matlab and tell this additional instance to run another job. Each instance of matlab open will use a different core. So if you have a quadcore, you can have 4 instances open and get 100% efficiency by running code in all 4.
So, I gained efficiency by having multiple instances of matlab open at the same time and running a job. My jobs took 8 to 27 hours at a time, and as one might imagine without liquid cooling I burnt out my cpu fan and had to replace it.
Also do look into optimizing your matlab code, I recently optimized my code and now it runs 40% faster.
I have noticed that the first time I run a script, it takes considerably more time than the second and third time1. The "warm-up" is mentioned in this question without an explanation.
Why does the code run faster after it is "warmed up"?
I don't clear all between calls2, but the input parameters change for every function call. Does anyone know why this is?
1. I have my license locally, so it's not a problem related to license checking.
2. Actually, the behavior doesn't change if I clear all.
One reason why it would run faster after the first time is that many things are initialized once, and their results are cached and reused the next time. For example in the M-side, variables can be defined as persistent in functions that can be locked. This can also occur on the MEX-side of things.
In addition many dependencies are loaded after the first time and remain so in memory to be re-used. This include M-functions, OOP classes, Java classes, MEX-functions, and so on. This applies to both builtin and user-defined ones.
For example issue the following command before and after running your script for the first run, then compare:
[M,X,C] = inmem('-completenames')
Note that clear all does not necessarily clear all of the above, not to mention locked functions...
Finally let us not forget the role of the accelerator. Instead of interpreting the M-code every time a function is invoked, it gets compiled into machine code instructions during runtime. JIT compilation occurs only for the first invocation, so ideally the efficiency of running object code the following times will overcome the overhead of re-interpreting the program every time it runs.
Matlab is interpreted. If you don't warm up the code, you will be losing a lot of time due to interpretation instead of the actual algorithm. This can skew results of timings significantly.
Running the code at least once will enable Matlab to actually compile appropriate code segments.
Besides Matlab-specific reasons like JIT-compilation, modern CPUs have large caches, branch predictors, and so on. Warming these up is an issue for benchmarking even in assembly language.
Also, more importantly, modern CPUs usually idle at low clock speed, and only jump to full speed after several milliseconds of sustained load.
Intel's Turbo feature gets even more funky: when power and thermal limits allow, the CPU can run faster than its sustainable max frequency. So the first ~20 seconds to 1 minute of your benchmark may run faster than the rest of it, if you aren't careful to control for these factors.
Another issue not mensioned by Amro and Marc is memory (pre)allocation.
If your script does not pre-allocate its memory it's first run would be very slow due to memory allocation. Once it completed its first iteration all memory is allocated, so consecutive invokations of the script would be more efficient.
An illustrative example
for ii = 1:1000
vec(ii) = ii; %// vec grows inside loop the first time this code is executed only
end
I often have to run calculation intensive simulations using Matlab. These simulations often take a long time and I expect my computer to use all its ressources in order for these simulations to be completed in as little time as possible.
However, when I open the Activity Monitor on my computer, processor usage is never above 55% and there is often about 1GB of unused RAM.
My question is: why is the processor not used to its full potential, and is there a safe and easy way to change this? Indeed, it would be great if I could get my simulations to be completed in half the time they currently take!
Its probably because you have a processer with multiple cores and that the code you are executing isn't written to run in multiple threads/processes. Unless you specifically write your code to take advantage of multiple cores it will only be able to use a single core at a single time.
A relatively easy way to enable parallel computing is to use the Parallel Computing Toolbox.
Additionally you might consider reading this: http://www.mathworks.com/company/newsletters/articles/parallel-matlab-multiple-processors-and-multiple-cores.html
I am working on a time series based calculation. Each iteration of the calculation is independent. Could anyone share some tips / online primers on using utilising parallel processing in Matlab? How can this be specified inside the actual code?
Since you have access to the Parallel toolbox, I suggest that you first check whether you can do it the easy way.
Basically, instead of writing
for i=1:lots
out(:,i)=do(something);
end
You write
parfor i=1:lots
out(:,i)=do(something);
end
Then, you use matlabpool to create a number of workers (you can have a maximum of 8 on your local machine with the toolbox, and tons on a remote cluster if you also have a Distributed Computing Server license), and you run the code, and see nice speed gains when your iterations are run by 8 cores instead of one.
Even though the parfor route is the easiest, it may not work right out of the box, since you might do your indexing wrong, or you may be referencing an array in a problematic way etc. Look at the mlint warnings in the editor, read the documentation, and rely on good old trial and error, and you should figure it out reasonably fast. If you have nested loops, it's often best parallelize only the innermost one and ensure it does tons of iterations - this is not only good design, it also reduces the amount of code that could give you trouble.
Note that especially if you run the code on a local machine, you may run into memory issues (which might manifest in really slow execution in parallel mode because you're paging): Every worker gets a copy of the workspace, so if your calculation involves creating a 500MB array, 8 workers will need a total 4GB of RAM - and then you haven't even started counting the RAM of the parent process! In addition, it can be good to only use N-1 cores on your machine, so that there is still one core left for other processes that may run on the computer (such as a mandatory antivirus...).
Mathworks offers its own parallel computing toolbox. If you do not want to purchase that, there a few options
You could write your own mex file and use pthreads or OpenMP.
However make sure you do not call any Mex api in the parallel part of the code, because they arent thread safe
If you want coarser grained parallelism via MPI you can try pmatlab
Same with parmatlab
Edit: Adding link Parallel MATLAB with openmp mex files
I have only tried the first.
Don't forget that many Matlab functions are already multithreaded. By careful programming you may be able to take advantage of them -- check the documentation for your version as the Mathworks seem to be increasing the range and number of multithreaded functions with each new release. For example, it seems that 2010a has multithreaded ffts which may be useful for time series processing.
If the intrinsic multithreading is not what you need, then as #srean suggests, the Parallel Computing Toolbox is available. For my money (or rather, my employers' money) it's the way to go, allowing you to program in parallel in Matlab, rather than having to bolt things on. I have to admit, too, that I'm quite impressed by the toolbox and the facilities it offers.