The MATLAB documentation describes the break keyword thus:
break terminates the execution of a for or while loop. Statements in the loop after the break statement do not execute.
In nested loops, break exits only from the loop in which it occurs. Control passes to the statement that follows the end of that loop.
(my emphasis)
What if you want to exit from multiple nested loops? Other languages, such as Java, offer labelled breaks, which allow you to specify where the flow of control is to be transferred, but MATLAB lacks such a mechanism.
Consider the following example:
% assume A to be a 2D array
% nested 'for' loops
for j = 1 : n
for i = 1 : m
if f(A(i, j)) % where f is a predicate
break; % if want to break from both loops, not just the inner one
else
% do something interesting with A
end
end
% <--- the break transfers control to here...
end
% <--- ... but I want to transfer control to here
What is an idiomatic way (in MATLAB) of exiting from both loops?
I would say for your original specific example, rather use linear indexing and a single loop:
%// sample-data generation
m = 4;
n = 5;
A = rand(m, n);
temp = 0;
for k = 1:numel(A)
if A(k) > 0.8 %// note that if you had switched your inner and outer loops you would have had to transpose A first as Matlab uses column-major indexing
break;
else
temp = temp + A(k);
end
end
Or the practically identical (but with less branching):
for k = 1:numel(A)
if A(k) <= 0.8 %// note that if you had switched your inner and outer loops you would have had to transpose A first as Matlab uses column-major indexing
temp = temp + A(k);
end
end
I would think that this answer will vary from case to case and there is no general one size fits all idiomatically correct solution but I would approach it in the following way depending on your problem (note these all assumes that a vectorized solution is not practical as that is the obvious first choice)
Reduce the dimensions of the nesting and use either no breaks or just one single break (i.e. as shown above).
Don't use break at all because unless the calculation of your predicate is expensive and your loop has very many iterations, those extra iterations at the end should be practically free.
Failing that set a flag and break at each level.
Or finally wrap your loop into a function and call return instead of break.
As far as I know there are no such functionality built in. However, in most cases matlab does not need nested loops due to its support for vectorization. In those cases where vectorization does not work, the loops are mostly long and complicated and thus multiple breaks would not hinder readability significantly. As noted in a comment, you would not really need a nested loop here. Vectorization would do the trick,
m = 5;
n=4;
x = rand(m,n);
tmp = find(x>0.8, 1, 'first');
if (isempty(tmp))
tmp = m*n+1;
end
tmp = tmp-1;
tot = sum(x(1:tmp));
There might of course be people claiming that for loops are not necessarily slow anymore, but the fact remains that Matlab is column heavy and using more than one loop will in most cases include looping over non optimal dimensions. Vectorized solutions does not require that since they can use smart methods avoiding such loops (which of course does not hold if the input is a row vector, so avoiding this is also good).
The best idiomatic way to use Python (or poison of your choice) and forget all this but that's another story. Also I don't agree with the vectorization claims of the other answers anymore. Recent matlab versions handle for loops pretty quickly. You might be surprised.
My personal preference goes to raising an exception deliberately and cradling it within a try and catch block.
% assume A to be a 2D array
A = rand(10) - 0.5;
A(3,2) = 0;
wreaker = MException('Loop:breaker','Breaking the law');
try
for j = 1 : size(A,1)
% forloop number 1
for i = 1 : size(A,2)
% forloop number 2
for k = 1:10
% forloop number 3
if k == 5 && j == 3 && i == 6
mycurrentval = 5;
throw(wreaker)
end
end
end
end
catch
return % I don't remember the do nothing keyword for matlab apparently
end
You can change the location of your try catch indentation to fall back to the loop of your choice. Also by slaying kittens, you can write your own exceptions such that they label the exception depending on the nest count and then you can listen for them. There is no end to ugliness though still prettier than having counters or custom variables with if clauses in my opinion.
Note that, this is exactly why matlab drives many people crazy. It silently throws exceptions in a pretty similar way and you get a nonsensical error for the last randomly chosen function while passing by, such as size mismatch in some differential equation solver so on. I actually learned all this stuff after reading a lot matlab toolbox source codes.
Related
I'm trying to run this code in Matlab
a = ones(4,4);
b=[1,0,0,1;0,0,0,1;0,1,0,0;0,0,0,0];
b(:,:,2)=[0,1,1,0;1,1,1,0;1,0,1,1;1,1,1,1];
parfor i = 1:size(b,3)
c = b(:,:,i)
a(c) = i;
end
but get the error:
Error: The variable a in a parfor cannot be classified.
See Parallel for Loops in MATLAB, "Overview".
There are restrictions in how you can write into arrays inside the body of a parfor loop. In general, you will need to use sliced arrays.
The reason behind this issue is that Matlab needs to prevent that different worksers access the same data, leading to unpredictable results (as the timely order in which the parfor loops through i is not detemined).
So, although in your example the workers don't operate on the same entries of a, due to the way how you index a (with an array of logicals), it is currently not possible for Matlab to decide if this is the case or not (in other words, Matlab cannot classify a).
Edit: For completeness I add some code that is equivalent to your example, although I assume that your actual problem involves more complicated logical indexing?
a = ones(4,4,4);
parfor i = 1:size(a,1)
a(i, :, :) = zeros(4, 4) + i; % this is sliced indexing
end
Edit: As the OP example was modified, the above code is not equivalent to the example anymore.
Suppose I have two functions written on different scripts, say function1.m and function2.m The two computation in the two functions are independent (Some inputs may be the same, say function1(x,y) and function2(x,z) for example). However, running sequentially, say ret1 = function1(x,y); ret2 = function2(x,z); may be time consuming. I wonder if it is possible to run it in parfor loop:
parfor i = 1:2
ret(i) = run(['function' num2str(i)]); % if i=1,ret(1)=function1 and i=2, ret(2)=function2
end
Is it possible to write it in parfor loop?
Your idea is correct, but the implementation is wrong.
Matlab won't let you use run within parfor as it can't make sure it's a valid way to use parfor (i.e. no dependencies between iterations). The proper way to do that is to use functions (and not scrips) and an if statement to choose between them:
ret = zeros(2,1);
parfor k = 1:2
if k==1, ret(k) = f1(x,y); end
if k==2, ret(k) = f2(x,z); end
end
here f1 and f2 are some functions that return a scalar value (so it's suitable for ret(k) and each instance of the loop call a different if statement.
You can read here more about how to convert scripts to functions.
The rule of thumb for a parfor loop is that each iteration must be standalone. More accurately,
The body of the parfor-loop must be independent. One loop iteration
cannot depend on a previous iteration, because the iterations are
executed in a nondeterministic order.
That means that every iteration must be one which can be performed on its own and produce the correct result.
Therefore, if you have code that says, for instance,
parfor (i = 1:2)
function1(iterator,someNumber);
function2(iterator,someNumber);
end
there should be no issue with applying parfor.
However, if you have code that says, for instance,
persistentValue = 0;
parfor (i = 1:2)
persistentValue = persistentValue + function1(iterator,someNumber);
function2(iterator,persistentValue);
end
it would not be usable.
Yes. It is possible.
Here's an example:
ret = zeros(2,1);
fHandles = {#min, #max};
x = 1:10;
parfor i=1:2
ret(i) = fHandles{i}(x);
end
ret % show the results.
Whether this is a good idea or not, I don't know. There is overhead to setting up the parallel processing that may or may not make it worthwhile for you.
Typically the more iterations you have computed, the more value you get from setting up a parfor loop as the iterations are sliced-up and sent non-deterministically to the separate cores for processing. So you're getting use of 2 cores right now, but if you have many functions this may improve things.
The order that the iterations are run is not guaranteed (it could be that one core gets assigned a range of values for i, but we do not know if it those values are taken in order or randomly), so your code can't depend on other iterations of the loop.
In general, the MATLAB editor is pretty at flagging these issues ahead of time.
EDIT
Here's a proof of concept for a variable number of arguments to your different functions
ret = zeros(2,1);
fHandles = {#min, #max};
x = 1:10; % x is a 1x10 vector
y = rand(20); % y is a 20x20 matrix
z = 1; % z is a scalar value
fArgs = {{x};
{y,z}}; %wrap your arguments up in a cell
parfor i=1:2
ret(i) = fHandles{i}([fArgs{i}{:}]); %calls the function with its variable sized arguments here
end
ret % show the output
Again, this is just proof-of-concept. There are big warnings showing up in MATLAB about having to broadcast fArgs across all of the cores.
The following code works, but if I change for into parfor, it gave an error
Index exceeds matrix dimensions
This is my code
a=zeros(3,1);
for t=1:2
ind=randsample(3,2)
a=pf(a,ind)
end
function a=pf(a,ind)
a(ind)=a(ind)+2;
end
How can I get this code working without the error?
You are seeing the error because you are misusing parfor in your code. You haven't read the relevant documentation enough, and you seem to believe that parfor is magic fairy dust that makes your computation faster, regardless of computation. Well, I have bad news.
Let's take a closer look at your example:
a = zeros(3,1);
% usual for
disp('before for')
for t=1:2
ind = randsample(3,2);
a = pf(a,ind);
disp(a); % add printing line
end
% parfor
disp('before parfor')
parfor t=1:2
ind = randsample(3,2);
a = pf(a,ind);
disp(a); % add printing line
end
The output:
before for
2
2
0
2
4
2
before parfor
Error: The variable a is perhaps intended as a reduction variable, but is actually an uninitialized temporary.
See Parallel for Loops in MATLAB, "Temporary Variables Intended as Reduction Variables".
As you can see, in the latter case there are no prints inside the parfor, so it doesn't even get run. See also the warning about the type of variables. The variable a is being misidentified by the execution engine because what you are doing to it doesn't make any sense.
So what to do instead? You need to formulate your problem in a way that is compatible with parfor. This will, alas, depend on what exactly you're doing to your matrix. For your specific case of incrementing random elements, I suggest that you gather the increments separately in the loop, and sum them up afterwards:
a = zeros(3,1); % only needed for size; assumed that it exists already
numiters = 2;
increments = zeros([size(a), numiters]); % compatible with a proper 2d array too
parfor t=1:numiters
ind = randsample(3,2);
% create an auxiliary increment array so that we can use a full slice of 'increments'
new_contrib = zeros(size(a));
new_contrib(ind) = 2;
increments(:,t) = new_contrib;
disp(increments(:,t)); % add printing line
end
% collect increments along last axis
a = sum(increments,ndims(increments));
disp(a)
Output:
2
0
2
2
2
0
4
2
2
Note the lack of warnings and the presence of a meaningful answer. Refactoring the loop this way transparently signals MATLAB what the variables are doing, and that increments is being filled up by independent iterations of the parfor loop. This is the way in which parfor can "speed up calculations", a very specific and controlled way that implies restrictions on the logistics used inside the loop.
n = 2;
a=zeros(3,1);
ind=zeros(3,2,n);
for ii = 1:n
ind(:,:,ii) = randsample(3,2);
end
for t=1:n
a=pf(a,ind(:,:,t));
end
function a=pf(a,ind)
a(ind)=a(ind)+2;
end
The above gets the randsample out of the loop, which is probably the issue here. Note that randsample does not support direct 3D matrix creation, so I initialised that in a loop.
I parallelised the code below but the simulation time is actually 400-500 times longer than the serial code. The only reason i can think of that can cause this is the message 'variable x is indexed but not sliced in parfor loop and 'variable p is indexed but not sliced in parfor loop. Can anyone verify whether this is the reason for the huge increase in simulation time or the way i parallelised the code.
p=(1,i) and x(1,i) are matrix with values set before hand.
nt=1;
nc=32;
time(1,1) = 0.0;
for t=dt:dt:0.1
nt=nt+1;
time(1,nt) = t;
disp(t);
for ii=2:nc
mytemp=zeros(1,ii);
dummy=0.0;
parfor jj=1:nc+1
if ii==jj % skipped
continue;
end
dxx = x(1,jj) - x(1,ii);
rr=abs(dxx);
if rr < re
dummy(jj) = (p(nt-1,jj)-p(nt-1,ii))*kernel(rr,re,ktype)*rr;
mytemp(jj) = kernel(rr,re,ktype)*rr;
%sumw(1,ii) = sumw(1,ii) + kernel(rr,re,1);
end
end
mysum = sum(dummy);
zeta(1,ii)=sum(mytemp);
lapp(1,ii) = 2.0*dim*mysum/zeta(1,ii);
p(nt,ii) = p(nt-1,ii) + dt*lapp(1,ii);
end
% update boundary value
p(nt,1) = function_phi(0,t);
p(nt,nc+1) = function_phi(1,t);
end
Can't be sure that is the reason, but if some parts of the code end up being parallelized while others cannot, it will create a lot of overhead without any speedup. See for example the Q&A here for a more detailed discussion of slicing.
Basically, if you have a parfor with a variable jj, then every statement in which jj is used on the right hand side should also use jj on the left hand side - in that way, the "job" can be divided between different processors, each of which tackles part of the array in parallel. As soon as that doesn't happen, for example in your lines
dxx = x(1,jj) - x(1,ii);
rr=abs(dxx);
you break the paradigm. 400x slower? I don't know about that - but the warning is pretty clear.
The first two lines could be consolidated, by the way, by computing rr(jj) (although you don't need an array):
rr(jj) = abs(x(1,jj) - x(1,ii));
You then use that value rather than rr later in the loop. This is a bit like having a private variable for each copy of the loop (a concept that I don't think Matlab has - but exists in OMP ).
I don't see where p is indexed in the parfor loop … it seems to be update outside of the inner loop, where it ought not to matter.
You might find it helpful to profile your code with the parallel profiler http://www.mathworks.com/help/distcomp/profiling-parallel-code.html - it will be instructive.
parfor is a convenient way to distribute independent iterations of intensive computations among several "workers". One meaningful restriction is that parfor-loops cannot be nested, and invariably, that is the answer to similar questions like there and there.
Why parallelization across loop boundaries is so desirable
Consider the following piece of code where iterations take a highly variable amount of time on a machine that allows 4 workers. Both loops iterate over 6 values, clearly hard to share among 4.
for row = 1:6
parfor col = 1:6
somefun(row, col);
end
end
It seems like a good idea to choose the inner loop for parfor because individual calls to somefun are more variable than iterations of the outer loop. But what if the run time for each call to somefun is very similar? What if there are trends in run time and we have three nested loops? These questions come up regularly, and people go to extremes.
Pattern needed for combining loops
Ideally, somefun is run for all pairs of row and col, and workers should get busy irrespectively of which iterand is being varied. The solution should look like
parfor p = allpairs(1:6, 1:6)
somefun(p(1), p(2));
end
Unfortunately, even if I knew which builtin function creates a matrix with all combinations of row and col, MATLAB would complain with an error The range of a parfor statement must be a row vector. Yet, for would not complain and nicely iterate over columns. An easy workaround would be to create that matrix and then index it with parfor:
p = allpairs(1:6, 1:6);
parfor k = 1:size(pairs, 2)
row = p(k, 1);
col = p(k, 2);
somefun(row, col);
end
What is the builtin function in place of allpairs that I am looking for? Is there a convenient idiomatic pattern that someone has come up with?
MrAzzman already pointed out how to linearise nested loops. Here is a general solution to linearise n nested loops.
1) Assuming you have a simple nested loop structure like this:
%dummy function for demonstration purposes
f=#(a,b,c)([a,b,c]);
%three loops
X=cell(4,5,6);
for a=1:size(X,1);
for b=1:size(X,2);
for c=1:size(X,3);
X{a,b,c}=f(a,b,c);
end
end
end
2) Basic linearisation using a for loop:
%linearized conventional loop
X=cell(4,5,6);
iterations=size(X);
for ix=1:prod(iterations)
[a,b,c]=ind2sub(iterations,ix);
X{a,b,c}=f(a,b,c);
end
3) Linearisation using a parfor loop.
%linearized parfor loop
X=cell(4,5,6);
iterations=size(X);
parfor ix=1:prod(iterations)
[a,b,c]=ind2sub(iterations,ix);
X{ix}=f(a,b,c);
end
4) Using the second version with a conventional for loop, the order in which the iterations are executed is altered. If anything relies on this you have to reverse the order of the indices.
%linearized conventional loop
X=cell(4,5,6);
iterations=fliplr(size(X));
for ix=1:prod(iterations)
[c,b,a]=ind2sub(iterations,ix);
X{a,b,c}=f(a,b,c);
end
Reversing the order when using a parfor loop is irrelevant. You can not rely on the order of execution at all. If you think it makes a difference, you can not use parfor.
You should be able to do this with bsxfun. I believe that bsxfun will parallelise code where possible (see here for more information), in which case you should be able to do the following:
bsxfun(#somefun,(1:6)',1:6);
You would probably want to benchmark this though.
Alternatively, you could do something like the following:
function parfor_allpairs(fun, num_rows, num_cols)
parfor i=1:(num_rows*num_cols)
fun(mod(i-1,num_rows)+1,floor(i/num_cols)+1);
end
then call with:
parfor_allpairs(#somefun,6,6);
Based on the answers from #DanielR and #MrAzzaman, I am posting two functions, iterlin and iterget in place of prod and ind2sub that allow iteration over ranges also if those do not start from one. An example for the pattern becomes
rng = [1, 4; 2, 7; 3, 10];
parfor k = iterlin(rng)
[plate, row, col] = iterget(rng, k);
% time-consuming computations here %
end
The script will process the wells in rows 2 to 7 and columns 3 to 10 on plates 1 to 4 without any workers idling while more wells are waiting to be processed. In hope that this helps someone, I deposited iterlin and iterget at the MATLAB File Exchange.