I have this part of code which is very long to run and I would like to know if it is possible to do an optimization or a vectorization for a faster running?
if intersect(pt, coord,'rows')
for t=1:size(pt,1)
for u=1:size(Mbb,1)
if pt(t,1)==Mbb(u,1)
img(pt(t,1),Mbb(u,2))=1;
end
end
end
end
Try multi-threading. Even on a single core multi-threading may increase the efficiency of your core. If you have a multi-core system then multi-threading will yield even more benefit. In MATLAB this is done using parfor. Note that this can only be done when there is no dependencies between loop iteration. Your code will have to look something like this. Sometimes the MATLAB interpreter will be over conservative in detecting dependencies and hence you have to write your loops in such a way that the interpreter doesnt see dependencies in iterations
if intersect(pt, coord,'rows')
loopsize=size(pt,1);
parfor t=1:loopsize
for u=1:size(Mbb,1)
if pt(t,1)==Mbb(u,1)
img(pt(t,1),Mbb(u,2))=1;
end
end
end
end
You spend much time comparing pt(t,1) and Mbb(u,1) to find matches, in a double loop. If the respective sizes are large, this can be costly (O(NM)).
What you can do is to pre-sort these arrays and search for equal values by a merge-like process, taking only O(N+M) operations.
Anyway, note that if the arrays pt and Mbb include many equal elements, which are also equal between the arrays, the problem can degenerate to NM matches. In this case, the sorting trick can't help.
I have written a code by matlab and i've used parallel computing toolbox
more description about my code:
i'm trying to implement parallel genetic algorithm by matlab and parallel computing toolbox.
i've implemented that but i've a problem. that is my parallel code with parfor is so much slower that serial one with for.
my code:
tic
for j=1:maxIteration
parfor i=1:numIslands
if migrationInterval
doMigration;
end
doCrossover;
doMutation;
newSpring;
end
end
toc
numIslands is always small number (5 to 12)
maxiteration is always big number (1500 to 5000)
please help me
thank you
I recommend you to run your function using "Run and Time" tool. The results will show, if the reason is in parfor-procedure or in your function.
It can be the parfor procedure is unnecesary and gives no advantage, but it depends always on the function you run.
You mention that your CPU has two cores. One problem might being the code itself, it looks as if you are calling scripts instead of functions, so you might be flooding your workspace unnecessarily by doing so. Furthermore, if any of those scripts is declaring variables on the fly, you might be clogging your RAM (Matlab is particularly good at that) thus making your code run slower.
Try to optimize each of the scripts first.
I would really recommend you to use functions instead of scripts though.
Some general Modelica advice?
We've built a model with ~2000 equations and three vectors of input from measured data. Using OpenModelica, attempts at simulation have begun to hang in the translation stage (which runs for hours where it used to take less than a minute) and now I regularly "lose connection to omc.exe." Is there perhaps something cumulative occurring that's degrading translation/compilation performance?
In general, are there any good rules of thumb for keeping simulations lighter and faster? I realize that, depending on the couplings, additional equations could be exponentially increasing the size of the resulting system of equations - could this be a problem?
Thanks for your thoughts!
It shouldn't take that long. Seems like a bug.
You can report this bug here:
https://trac.openmodelica.org/OpenModelica (New Ticket).
If your model is public you can post it there, if not you can contact the OpenModelica team privately.
I did some cleaning in the code; and got the part that repeats 12x (the module) down to ~180 equations; in the process I reduced the size of my input vectors (and also a 2D look-up table the module refers to) by quite a bit - they're both down to a few hundred values. It's working now--simulations run in reasonable time, a few minutes each.
Since all these tables were defined within Modelica functions (as you pointed out, Mr. Tiller) perhaps shrinking them helped to improve the performance. I had assumed that all that data just got spread out in a memory array, without going through any real processing, but maybe that's not the case...time to know more about what's going on under the hood in this environment (as always).
Thanks for the help!
so I have the following Integral that i need to do numerically:
Int[Exp(0.5*(aCosx + bSinx + cCos2x + dSin2x))] x=0..2Pi
The problem is that the output at any given value of x can be extremely large, e^2000, so larger than I can deal with in double precision.
I havn't had much luck googling for the following, how do you deal with large numbers in fortran, not high precision, i dont care if i know it to beyond double precision, and at the end i'll just be taking the log, but i just need to be able to handle the large numbers untill i can take the log..
Are there integration packes that have the ability to handle arbitrarily large numbers? Mathematica clearly can.. so there must be something like this out there.
Cheers
This is probably an extended comment rather than an answer but here goes anyway ...
As you've already observed Fortran isn't equipped, out of the box, with the facility for handling such large numbers as e^2000. I think you have 3 options.
Use mathematics to reduce your problem to one which does (or a number of related ones which do) fall within the numerical range that your Fortran compiler can compute.
Use Mathematica or one of the other computer algebra systems (eg Maple, SAGE, Maxima). All (I think) of these can be integrated into a Fortran program (with varying degrees of difficulty and integration).
Use a library for high-precision (often called either arbitray-precision or multiple-precision too) arithmetic. Your favourite search engine will turn up a number of these for you, some written in Fortran (and therefore easy to integrate), some written in C/C++ or other languages (and therefore slightly harder to integrate). You might start your search at Lawrence Berkeley or the GNU bignum library.
(Yes I know that I wrote that you have 3 options, but your question suggests that you aren't ready to consider this yet) You could write your own high-/arbitrary-/multiple-precision functions. Fortran provides everything you need to construct such a library, there is a lot of work already done in the field to learn from, and it might be something of interest to you.
In practice it generally makes sense to apply as much mathematics as possible to a problem before resorting to a computer, that process can not only assist in solving the problem but guide your selection or construction of a program to solve what's left of the problem.
I agree with High Peformance Mark that the best option here numerically is to use analytics to scale or simplify the result first.
I will mention that if you do want to brute force it, gfortran (as of 4.6, with the libquadmath library) has support for quadruple precision reals, which you can use by selecting the appropriate kind. As long as your answers (and the intermediate results!) don't get too much bigger than what you're describing, that may work, but it will generally be much slower than double precision.
This requires looking deeper at the problem you are trying to solve and the behavior of the underlying mathematics. To add to the good advice already provided by Mark and Jonathan, consider expanding the exponential and trig functions into Taylor series and truncating to the desired level of precision.
Also, take a step back and ask why you are trying to accomplish by calculating this value. As an example, I recently had to debug why I was getting outlandish results from a property correlation which was calculating vapor pressure of a fluid to see if condensation was occurring. I spent a long time trying to understand what was wrong with the temperature being fed into the correlation until I realized the case causing the error was a simulation of vapor detonation. The problem was not in the numerics but in the logic of checking for condensation during a literal explosion; physically, a condensation check made no sense. The real problem was the code was asking an unnecessary question; it already had the answer.
I highly recommend Forman Acton's Numerical Methods That (Usually) Work and Real Computing Made Real. Both focus on problems like this and suggest techniques to tame ill-mannered computations.
I'm trying to figure out the best programming language for an analytical model I'm building. Primary consideration is speed at which it will run FOR loops.
Some detail:
The model needs to perform numerous (~30 per entry, over 12 cycles) operations on a set of elements from an array -- there are ~300k rows, and ~150 columns in the array. Most of these operations are logical in nature, e.g., if place(i) = 1, then j(i) = 2.
I've built an earlier version of this model using Octave -- to run it takes ~55 hours on an Amazon EC2 m2.xlarge instance (and it uses ~10 GB of memory, but I'm perfectly happy to throw more memory at it). Octave/Matlab won't do elementwise logical operations, so a large number of for loops are needed -- I'm relatively certain that I've vectorized as much as possible -- the loops that remain are necessary. I've gotten octave-multicore to work with this code, which makes some improvement (~30% speed reduction when I get it running on 8 EC2 cores), but ends up being unstable with file locking, etc.
+I'm really looking for a step change in run-time -- I know that actually using Matlab might get me as much as a 50% improvement from looking at some benchmarks, but that is cost-prohibitive. The original plan when starting this was to actually run a Monte Carlo with this, but at 55 hours a run, that's completely impractical.
The next version of this is going to be a complete rebuild from the ground up (for IP reasons I won't get in to if nothing else), so I'm completely open to any programming language. I'm most familiar with Octave/Matlab, but have dabbled in R, C, C++, Java. I'm also proficient w/ SQL if the solution involves storing the data in a database. I'll learn any language for this -- these aren't complicated functionality we're looking for, no interfacing with other programs, etc., so not too concerned about learning curve.
So with all that said, what's the fastest programming language specifically for FOR loops? From a search of SO and Google, Fortran and C bubble to the top, but looking for some more advice before diving in to one or the other.
Thanks!
This for loop looks no more complex than this when it hits the CPU:
for(int i = 0; i != 1024; i++) translates to
mov r0, 0 ;;start the counter
top:
;;some processing
add r0, r0, 1 ;;increment the counter by 1
jne top: r0, 1024 ;;jump to the loop top if we havn't hit the top of the for loop (1024 elements)
;;continue on
As you can tell, this is sufficiently simple you can't really optimize it very well[1]... Refocus towards the algorithm level.
The first cut at the problem is to look at cache locality. Look up the classic example of matrix multiplication and swapping the i and j indexes.
edit: As a second cut, I would suggest evaluating the algorithm for data-dependencies between iterations and data dependency between localities in your 'matrix' of data. It may be a good candidate for parallelization.
[1] There are some micro-optimizations possible, but those will not produce the speedsups you're looking for.
~300k * ~150 * ~30 * ~12 = ~16G iterations, right?
This number of primitive operations should complete in a matter of minutes (if not seconds) in any compiled language on any decent CPU.
Fortran, C/C++ should do it almost equally well. Even managed languages, such as Java and C#, would only fall behind by a small margin (if at all).
If you have a problem of ~16G iterations running 55 hours, this means that they are very far from being primitive (80k per second? this is ridiculous), so maybe we should know more. (as was already suggested, is disk access limiting performance? is it network access?)
As #Rotsor said, 16G operations / 55 hours is about 80,000 operations per second, or one operation every 12.5 microseconds. That's a lot of time per operation.
That means your loops are not the cause of poor performance, it's what's in the innermost loop that's taking the time. And Octave is an interpreted language. That alone means an order of magnitude slowdown.
If you want speed, you first need to be in a compiled language. Then you need to do performance tuning (aka profiling) or, just single step it in a debugger at the instruction level. That will tell you what it is actually doing in its heart of hearts. Once you've got it to where it's not wasting cycles, fancier hardware, cores, CUDA, etc. will give you a parallelism speedup. But it's silly to do that if your code is taking unnecessarily many cycles. (Here's an example of performance tuning - a 43x speedup just by trimming the fat.)
I can't believe the number of responders talking about matlab, APL, and other vectorized languages. Those are interpreters. They give you concise source code, which is not at all the same thing as fast execution. When it comes down to the bare metal, they are stuck with the same hardware as every other language.
Added: to show you what I mean, I just ran this C++ code, which does 16G operations, on this moldy old laptop, and it took 94 seconds, or about 6ns per iteration. (I can't believe you baby-sat that thing for 2 whole days.)
void doit(){
double sum = 0;
for (int i = 0; i < 1000; i++){
for (int j = 0; j < 16000000; j++){
sum += j * 3.1415926;
}
}
}
In terms of absolute speed, probably Fortran followed by C, followed by C++. In practical application, well written code in any of the three, compiled with a descent compiler should be plenty fast.
Edit- generally you are going to see much better performance with any kind of looped or forking (e.g.- if statements) code with a compiled language, versus an interpreted language.
To give an example, on a recent project I was working on, the data sizes and operations were about 3/4 the size of what you're talking about here, but like your code, had very little room for vectorization, and required significant looping. After converting the code from matlab to C++, runtimes went from 16-18 hours, down to around 25 minutes.
For what you're discussing, Fortran is probably your first choice. The closest second place is probably C++. Some C++ libraries use "expression templates" to gain some speed over C for this kind of task. It's not entirely certain those will do you any good, but C++ can be at least as fast as C, and possibly somewhat faster.
At least in theory, there's no reason Ada couldn't be competitive as well, but it's been so long since I used it for anything like this that I hesitate to recommend it -- not because it isn't good, but because I just haven't kept track of current Ada compilers well enough to comment on them intelligently.
Any compiled language should perform the loop itself on roughly equal terms.
If you can formulate your problem in its terms, you might want to look at CUDA or OpenCL and run your matrix code on the GPU - though this is less good for code with lots of conditionals.
If you want to stay on conventional CPUs, you may be able to formulate your problem in terms of SSE scatter/gather and bitmask operations.
Probably the assembly language for whatever your platform is. But compilers (especially special-purpose ones that specifically target a single platform (e.g., Analog Devices or TI DSPs)) are often as good as or better than humans. Also, compilers often know about tricks that you don't. For example, the aforementioned DSPs support zero-overhead loops and the compiler will know how to optimize code to use those loops.
Matlab will do element-wise logical operations and they are generally quite fast.
Here is a quick example on my computer (AMD Athalon 2.3GHz w/3GB) :
d=rand(300000,150);
d=floor(d*10);
>> numel(d(d==1))
ans =
4501524
>> tic;d(d==1)=10;toc;
Elapsed time is 0.754711 seconds.
>> numel(d(d==1))
ans =
0
>> numel(d(d==10))
ans =
4501524
In general I've found matlab's operators are very speedy, you just have to find ways to express your algorithms directly in terms of matrix operators.
C++ is not fast when doing matrixy things with for loops. C is, in fact, specifically bad at it. See good math bad math.
I hear that C99 has __restrict pointers that help, but don't know much about it.
Fortran is still the goto language for numerical computing.
How is the data stored? Your execution time is probably more effected by I/O (especially disk or worse, network) than by your language.
Assuming operations on rows are orthogonal, I would go with C# and use PLINQ to exploit all the parallelism I could.
Might you not be best with a hand-coded assembler insert? Assuming, of course, that you don't need portability.
That and an optimized algorithm should help (and perhaps restructuring the data?).
You might also want to try multiple algorithms and profile them.
APL.
Even though it's interpreted, its primitive operators all operate on arrays natively, therefore you rarely need any explicit loops. You write the same code, whether the data is scalar or array, and the interpreter takes care of any looping needed internally, and thus with the minimum overhead - the loops themselves are in a compiled language, and will have been heavily optimised for the specific architecture of the CPU it's running on.
Here's an example of the simplicity of array handling in APL:
A <- 2 3 4 5 6 8 10
((2|A)/A) <- 0
A
2 0 4 0 6 8 10
The first line sets A to a vector of numbers.
The second line replaces all the odd numbers in the vector with zeroes.
The third line queries the new values of A, and the fourth line is the resulting output.
Note that no explicit looping was required, as scalar operators such as '|' (remainder) automatically extend to arrays as required. APL also has built-in primitives for searching and sorting, which will probably be faster than writing your own loops for these operations.
Wikipedia has a good article on APL, which also provides links to suppliers such as IBM and Dyalog.
Any modern compiled or JITted language is going to render down to pretty much the same machine language code, giving a loop overhead of 10 nano seconds or less, per iteration, on modern processors.
Quoting #Rotsor:
If you have a problem of ~16G iterations running 55 hours, this means that they are very far from being primitive (80k per second? this is ridiculous), so maybe we should know more.
80k operations per second is around 12.5 microseconds each - a factor of 1000 greater than the loop overhead you'd expect.
Assuming your 55 hour runtime is single threaded, and if your per item operations are as simple as suggested, you should be able to (conservatively) achieve a speedup of 100x and cut it down to under an hour very easily.
If you want to run faster still, you'll want to look at writing multi-threaded solution, in which case a language that provides good support would be essential. I'd lean towards PLINQ and C# 4.0, but that's because I already know C# - YMMV.
what about a lazy loading language like clojure. it is a lisp so like most lisp dialects lacks a for loop but has many other forms that operate more idiomatically for list processing. It might help your scaling issues as well because the operations are thread safe and because the language is functional it has fewer side effects. If you wanted to find all the items in the list that were 'i' values to operate on them you might do something like this.
(def mylist ["i" "j" "i" "i" "j" "i"])
(map #(= "i" %) mylist)
result
(true false true true false true)