I have implemented a Naive Bayes classifier. On Matlab, my classify function takes 2 minutes to run while octave takes 25 minutes to run the same code. Does anyone know what causes ocatve to run slower so that I can tweak my code accordingly?
PS: I have to submit to a server which runs octave and not Matlab.
Matlab does a lot of "hidden" optimization when running your code (Octave probably, too, but different ones). Many of these optimizations e.g. concern that parameters to functions are not copied if you do not modify these parameters in the function, but instead passed by reference. This can significantly speed up calculations when you e.g. pass around large matrices, since otherwise most of your computational time is spend on copying. There are many, many similar optimizations, and not all of them are documented at all.
Without specific knowledge of what you are computing, it's hard to guess where the difference comes from. I am not aware if octave has an equivalence to the matlab profiler, but if, I would use this to find out where octave spends all the time. For debugging, I would also recommend to download Octave to your PC and debug there.
Related
If I assume that a problem is a candidate for parallization e.g. matrix multiplication or some other problem and I use an Intel i7 haswell dualcore, is there some way I can compare a parallel execution to a sequential version of the same program or will matlab optimize a program to my architecture (dualcore, quadcore..)? I would like to know the speedup from adding more processors from a good benchmark parallell program.
Unfortunately there is no such thing as a benchmark parallel program. If you measure a speedup for a benchmark algorithm that does not mean that all the algorithms will benefit from parallelization
Since your target architecture has only 2 cores you might be better off avoiding parallelization at all and let Matlab and the operative system to optimize the execution. Anyway, here are the steps I followed.
Determine if your problem is apt for parallelization by calculating the theoretical speedup. Some problems like matrix multiplication or Gauss elimination are well studied. Since I assume your problem is more complicated than that, try to decompose your algorithm into simple blocks and determine, block-wise, the advantages of parallelization.
If you find that several parts of your algorithms could profit from parallelization, study those part separately.
Obtain statistical information of the runtime of your sequential algorithm. That is, run your program X number of times under similar conditions (and similar inputs) and average the running time.
Obtain statistical information of the runtime of your parallel algorithm.
Measure with the profiler. Many people recommends to use function like tic or toc. The profiler will give you a more accurate picture of your running times, as well as detailed information per function. See the documentation for detailed information on how to use the profiler.
Don't make the mistake of not taking into account the time Matlab takes to open the pool of workers (I assume you are working with the Parallel Computing Toolbox). Depending on your number of workers, the pool takes more/less time and in some occasions it could be up to 1 minute (2011b)!
You can try "Run and time" feature on MATLAB.
Or simply put some tic and toc to the first and end of your code, respectively.
Matlab provides a number of timing functions to help you assess the performance of your code: go read the documentation here and select the function that you deem most appropriate in your case! In particular, be aware of the difference between tic toc and the cputime function.
Are there any faster and more efficient solvers other than fmincon? I'm using fmincon for a specific problem and I run out of memory for modest sized vector variable. I don't have any supercomputers or cloud computing options at my disposal, either. I know that any alternate solution will still run out of memory but I'm just trying to see where the problem is.
P.S. I don't want a solution that would change the way I'm approaching the actual problem. I know convex optimization is the way to go and I have already done enough work to get up until here.
P.P.S I saw the other question regarding the open source alternatives. That's not what I'm looking for. I'm looking for more efficient ones, if someone faced the same problem adn shifted to a better solver.
Hmmm...
Without further information, I'd guess that fmincon runs out of memory because it needs the Hessian (which, given that your decision variable is 10^4, will be 10^4 x numel(f(x1,x2,x3,....)) large).
It also takes a lot of time to determine the values of the Hessian, because fmincon normally uses finite differences for that if you don't specify derivatives explicitly.
There's a couple of things you can do to speed things up here.
If you know beforehand that there will be a lot of zeros in your Hessian, you can pass sparsity patterns of the Hessian matrix via HessPattern. This saves a lot of memory and computation time.
If it is fairly easy to come up with explicit formulae for the Hessian of your objective function, create a function that computes the Hessian and pass it on to fmincon via the HessFcn option in optimset.
The same holds for the gradients. The GradConstr (for your non-linear constraint functions) and/or GradObj (for your objective function) apply here.
There's probably a few options I forgot here, that could also help you. Just go through all the options in the optimization toolbox' optimset and see if they could help you.
If all this doesn't help, you'll really have to switch optimizers. Given that fmincon is the pride and joy of MATLAB's optimization toolbox, there really isn't anything much better readily available, and you'll have to search elsewhere.
TOMLAB is a very good commercial solution for MATLAB. If you don't mind going to C or C++...There's SNOPT (which is what TOMLAB/SNOPT is based on). And there's a bunch of things you could try in the GSL (although I haven't seen anything quite as advanced as SNOPT in there...).
I don't know on what version of MATLAB you have, but I know for a fact that in R2009b (and possibly also later), fmincon has a few real weaknesses for certain types of problems. I know this very well, because I once lost a very prestigious competition (the GTOC) because of it. Our approach turned out to be exactly the same as that of the winners, except that they had access to SNOPT which made their few-million variable optimization problem converge in a couple of iterations, whereas fmincon could not be brought to converge at all, whatever we tried (and trust me, WE TRIED). To this day I still don't know exactly why this happens, but I verified it myself when I had access to SNOPT. Once, when I have an infinite amount of time, I'll find this out and report this to the MathWorks. But until then...I lost a bit of trust in fmincon :)
I wrote a code to solve a system using ode45 and ode15s in matlab. I am wondering if I can improve the speed of the code using multiple core (or parallel code) in my script.
Anyone have tried this ??
Thanks
No, you can't.
All numerical integrators, ode45 and friends included, use some form of iterative scheme to solve the user-implemented (coupled) non-linear (partial) differential equations.
Each new step in the iterative schemes of ode45/15s/.. (to compute the new state of the system) depends on the previous step (the old state of the system), therefore, these numerical integrators cannot be parallelized effectively.
The only speedup you can do that's likely to have a big impact is to optimize your implementation of the differential equation.
From my experience, the only way to use multiple cores for ODE suite solvers in MATLAB is to use "parfor loop" to start multiple computations together at the same time, your single computation not be any faster, but you can start many with different parameters and have multiple solutions after that long wait. So if you need to start ODE many times that might speed up your work.
To speed up one ODE function it also a good idea to play with RelTol and AbsTol settings (changes time form seconds to hours), using Jpattern option can also be very helpful (my almost tridiagonal pattern made it run twice as fast). If your differential equation is simple maybe try to compile it first, or at least vectorize (I used to write some part of code in Java and then point MATLAB to use compiled .class file). Obviously the length of your solution vector plays important role, so don't make it more than a few hounded.
I need to run several independent analyses on the same data set.
Specifically, I need to run bunches of 100 glm (generalized linear models) analyses and was thinking to take advantage of my video card (GTX580).
As I have access to Matlab and the Parallel Computing Toolbox (and I'm not good with C++), I decided to give it a try.
I understand that a single GLM is not ideal for parallel computing, but as I need to run 100-200 in parallel, I thought that using parfor could be a solution.
My problem is that it is not clear to me which approach I should follow. I wrote a gpuArray version of the matlab function glmfit, but using parfor doesn't have any advantage over a standard "for" loop.
Has this anything to do with the matlabpool setting? It is not even clear to me how to set this to "see" the GPU card. By default, it is set to the number of cores in the CPU (4 in my case), if I'm not wrong.
Am I completely wrong on the approach?
Any suggestion would be highly appreciated.
Edit
Thanks. I'm aware of GPUmat and Jacket, and I could start writing in C without too much effort, but I'm testing the GPU computing possibilities for a department where everybody uses Matlab or R. The final goal would be a cluster based on C2050 and the Matlab Distribution Server (or at least this was the first project).
Reading the ADs from Mathworks I was under the impression that parallel computing was possible even without C skills. It is impossible to ask the researchers in my department to learn C, so I'm guessing that GPUmat and Jacket are the better solutions, even if the limitations are quite big and the support to several commonly used routines like glm is non-existent.
How can they be interfaced with a cluster? Do they work with some job distribution system?
I would recommend you try either GPUMat (free) or AccelerEyes Jacket (buy, but has free trial) rather than the Parallel Computing Toolbox. The toolbox doesn't have as much functionality.
To get the most performance, you may want to learn some C (no need for C++) and code in raw CUDA yourself. Many of these high level tools may not be smart enough about how they manage memory transfers (you could lose all your computational benefits from needlessly shuffling data across the PCI-E bus).
Parfor will help you for utilizing multiple GPUs, but not a single GPU. The thing is that a single GPU can do only one thing at a time, so parfor on a single GPU or for on a single GPU will achieve the exact same effect (as you are seeing).
Jacket tends to be more efficient as it can combine multiple operations and run them more efficiently and has more features, but most departments already have parallel computing toolbox and not jacket so that can be an issue. You can try the demo to check.
No experience with gpumat.
The parallel computing toolbox is getting better, what you need is some large matrix operations. GPUs are good at doing the same thing multiple times, so you need to either combine your code somehow into one operation or make each operation big enough. We are talking a need for ~10000 things in parallel at least, although it's not a set of 1e4 matrices but rather a large matrix with at least 1e4 elements.
I do find that with the parallel computing toolbox you still need quite a bit of inline CUDA code to be effective (it's still pretty limited). It does better allow you to inline kernels and transform matlab code into kernels though, something that
I'm going to attempt to optimize some code written in MATLAB, by using CUDA. I recently started programming CUDA, but I've got a general idea of how it works.
So, say I want to add two matrices together. In CUDA, I could write an algorithm that would utilize a thread to calculate the answer for each element in the result matrix. However, isn't this technique probably similar to what MATLAB already does? In that case, wouldn't the efficiency be independent of the technique and attributable only to the hardware level?
The technique might be similar but remember with CUDA you have hundreds of threads running simultaneously. If MATLAB is using threads and those threads are running on a Quad core, you are only going to get 4 threads excuted per clock cycle while you might achieve a couple of hundred threads to run on CUDA with that same clock cycle.
So to answer you question, YES, the efficiency in this example is independent of the technique and attributable only to the hardware.
The answer is unequivocally yes, all the efficiencies are hardware level. I don't how exactly matlab works, but the advantage of CUDA is that mutltiple threads can be executed simultaneously, unlike matlab.
On a side note, if your problem is small, or requires many read write operations, CUDA will probably only be an additional headache.
CUDA has official support for matlab.
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You can make use of mex files to run on GPU from MATLAB.
The bottleneck is the speed at which data is transfered from CPU-RAM to GPU. So if the transfer is minimized and done in large chunks, the speedup is great.
For simple things, it's better to use the gpuArray support in the Matlab PCT. You can check it here
http://www.mathworks.de/de/help/distcomp/using-gpuarray.html
For things like adding gpuArrays, multiplications, mins, maxs, etc., the implementation they use tends to be OK. I did find out that for making things like batch operations of small matrices like abs(y-Hx).^2, you're better off writing a small Kernel that does it for you.