I am new here for asking questions though I have found solutions to many problems here before.
This particular problem I cannot seem to find an answer for so I though I would join up and ask.
I am using the Parallel computing toolbox to run multiple simulations at once, the code I am developing is to be deployed on a single core so there is no need for converting the algorithm to parallel.
The data structures being created by each of the simulations are large, and running 8 simulations at once is using all of the available RAM in my machine (4GB).
I am currently looking at reducing the memory used by each simulation, and was wondering if anybody knew how to get memory usage info from each of the instances of the function.
So far I have been calling:
parfor i=1:8
[IR(:, i) Data(i)] = feval(F, NX, NY(i), SR, NS, i);
end
And inside function F
[usr, sys] = memory;
format short eng;
TEST.Mem = usr.MemUsedMATLAB;
But this understandably is returning the memory being used by all 8 instances of F.
I would like to get the information from each instance of F.
Note: The data structure TEST is returned as Data to the top level function.
Thanks in advance for any help.
you can use the matlab profiler to get a hint at the memory usage:
% start profiler
profile -memory on;
% start your simulation
my_sim();
% look at profiler results
profile viewer
Related
By assigning a matrix into a much bigger allocated memory, matlab somehow will duplicate it while 'copying' it, and if the matrix to be copied is large enough, there will be memory overflow. This is the sample code:
main_mat=zeros(500,500,2000);
n=500;
slice_matrix=zeros(500,500,n);
for k=1:4
parfor i=1:n
slice_matrix(:,:,i)=gather(gpuArray(rand(500,500)));
end
main_mat(:,:,1+(k-1)*n:1+(k-1)*n+n-1)=slice_matrix; %This is where the memory will likely overflow
end
Any way to just 'smash' the slice_matrix onto the main_mat without the overhead? Thanks in advance.
EDIT:
The overflow occurred when main_mat is allocated beforehand. If main_mat is initialized with main_mat=zeros(500,500,1); (smaller size), the overflow will not occur, but it will slowed down as allocation is not done before matrix is assigned into it. This will significantly reduce the performance as the range of k increases.
The main issue is that numbers take more space than zeros.
main_mat=zeros(500,500,2000); takes little RAM while main_mat = rand(500,500,2000); take a lot, no matter if you use GPU or parfor (in fact, parfor will make you use more RAM). So This is not an unnatural swelling of memory. Following Daniel's link below, it seems that the assignment of zeros only creates pointers to memory, and the physical memory is filled only when you use the matrix for "numbers". This is managed by the operating system. And it is expected for Windows, Mac and Linux, either you do it with Matlab or other languages such as C.
Removing parfor will likely fix your problem.
parfor is not useful there. MATLAB's parfor does not use shared memory parallelism (i.e. it doesn't start new threads) but rather distributed memory parallelism (it starts new processes). It is designed to distribute work over a set or worker nodes. And though it also works within one node (or a single desktop computer) to distribute work over multiple cores, it is not an optimal way of doing parallelism within one node.
This means that each of the processes started by parfor needs to have its own copy of slice_matrix, which is the cause of the large amount of memory used by your program.
See "Decide When to Use parfor" in the MATLAB documentation to learn more about parfor and when to use it.
I assume that your code is just a sample code and that rand() represents a custom in your MVE. So there are a few hints and tricks for the memory usage in matlab.
There is a snippet from The MathWorks training handbooks:
When assigning one variable to another in MATLAB, as occurs when passing parameters into a function, MATLAB transparently creates a reference to that variable. MATLAB breaks the reference, and creates a copy of that variable, only when code modifies one or more of teh values. This behavior, known as copy-on-write, or lazy-copying, defers the cost of copying large data sets until the code modifies a values. Therefore, if the code performs no modifications, there is no need for extra memory space and execution time to copy variables.
The first thing to do would be to check the (memory) efficiency of your code. Even the code of excellent programmers can be futher optimized with (a little) brain power. Here are a few hints regarding memory efficiency
make use of the nativ vectorization of matlab, e.g. sum(X,2), mean(X,2), std(X,[],2)
make sure that matlab does not have to expand matrices (implicit expanding was changed recently). It might be more efficient to use the bsxfun
use in-place-operations, e.g. x = 2*x+3 rather than x = 2*x+3
...
Be aware that optimum regarding memory usage is not the same as if you would want to reduce computation time. Therefore, you might want to consider reducing the number of workers or refrain from using the parfor-loop. (As parfor cannot use shared memory, there is no copy-on-write feature with using the Parallel Toolbox.
If you want to have a closer look at your memory, what is available and that can be used by Matlab, check out feature('memstats'). What is interesting for you is the Virtual Memory that is
Total and available memory associated with the whole MATLAB process. It is limited by processor architecture and operating system.
or use this command [user,sys] = memory.
Quick side node: Matlab stores matrices consistently in memory. You need to have a large block of free RAM for large matrices. That is also the reason why you want to allocate variables, because changing them dynamically forces Matlab to copy the entire matrix to a larger spot in the RAM every time it outgrows the current spot.
If you really have memory issues, you might just want to dig into the art of data types -- as is required in lower level languages. E.g. you can cut your memory usage in half by using single-precision directly from the start main_mat=zeros(500,500,2000,'single'); -- btw, this also works with rand(...,'single') and more native functions -- although a few of the more sophisticated matlab functions require input of type double, which you can upcast again.
If I understand correctly your main issue is that parfor does not allow to share memory. Think of every parfor worker as almost a separate matlab instance.
There is basically just one workaround for this that I know (that I have never tried), that is 'shared matrix' on Fileexchange: https://ch.mathworks.com/matlabcentral/fileexchange/28572-sharedmatrix
More solutions: as others suggested: remove parfor is certainly one solution, get more ram, use tall arrays (that use harddrives when ram runs full, read here), divide operations in smaller chunks, last but not least, consider an alternative other than Matlab.
You may use following code. You actually don't need the slice_matrix
main_mat=zeros(500,500,2000);
n=500;
slice_matrix=zeros(500,500,n);
for k=1:4
parfor i=1:n
main_mat(:,:,1+(k-1)*n + i - 1) = gather(gpuArray(rand(500,500)));
end
%% now you don't need this main_mat(:,:,1+(k-1)*n:1+(k-1)*n+n-1)=slice_matrix; %This is where the memory will likely overflow
end
In my current setup I have a for loop in which I extract different type of data from a SQL database hosted on Amazon EC2. This extraction is done in the function extractData(variableName). After that the data gets parsed and stored as a mat file in parsestoreData(data):
variables = {'A','B','C','D','E'}
for i = 1:length(variables)
data = extractData(variables{i});
parsestoreData(data);
end
I would like to parallelize this extraction and parsing of the data and to speed up the process. I argue that I could do this using a parfor instead of for in the above example.
However, I am worried that the extraction will not be improved as the SQL database will get slowed down when multiple requests are made on the same database.
I am therefore wondering if Matlab can handle this issue in a smart way, in terms of parralelization?
The workers in parallel pool running parfor are basically full MATLAB processes without a UI, and they default to running in "single computational thread" mode. I'm not sure whether parfor will benefit you in this case - the parfor loop simply arranges for the MATLAB workers to execute the iterations of your loop in parallel. You can estimate for yourself how well your problem will parallelise by launching multiple full desktop MATLABs, and set them off running your problem simultaneously. I would run something like this:
maxNumCompThreads(1);
while true
t = tic();
data = extractData(...);
parsestoreData(data);
toc(t)
end
and then check how the times reported by toc vary as the number of MATLAB clients varies. If the times remain constant, you could reasonably expect parfor to give you benefit (because it means the body can be parallelised effectively). If however, the times decrease significantly as you run more MATLAB clients, then it's almost certain that parfor would experience the same (relative) slow-down.
My code is:
function eigs_mem_test
N = 20000;
density = 0.2;
numOfModes = 250;
A = sprand(N, N, density);
profile -memory on
eigs(A, numOfModes, 0.0)
profile off
profsave(profile('info'), 'eigs_test')
profview
end
And this returns
i.e. it says that MATLAB allocated 18014398508117708.00 Kb or 1.8e10 Gb -- completely impossible. How did this happen? The code finishes with correct output and in htop I can see the memory usage vary quite a bit, but staying under 16G.
For N = 2000, I get sensible results (i.e. 0.2G allocated.)
How can I profile this case effectively, if I want to obtain an upper bound on memory used for large sparse matrices?
I use MATLAB R2017a.
I cannot reproduce your issue in R2017b, with 128GB of RAM on my machine. Here is the result after running your example code:
Notably, the function peaked at 14726148Kb, or ~1.8GB. I'm more confused by the units MATLAB has used here, as I saw nearer 14GB of usage in the task manager, which matches your large observed usage (and 1.4e7KB in GB), I can only think the profiler is meant to state KB (kilobytes) instead of Kb (kilobits).
Ridiculously large, unexpected values like this are often the result of overflow, so this could be an internal overflow bug.
You could use whos to get the size on disk of a variable
w = whos('A'); % get details of variable A
sizeOnDisk = w.bytes; % get size on disk
This doesn't necessarily tell you how much memory a function like eigs in your example uses though. You could poll memory within your function to get the current usage.
I'll resist exploring this further, since the question of how to profile for memory usage has already been asked and answered.
N.B. I'm not sure why my machine was ~100x slower than yours, I assume the image of your memory usage didn't come from actually running your example code? Or my RAM is awful...
Is it any way in matlab that after program is finished to run, find the memory and time?
Also, if the workplace is saved and then it is loaded again, is it possible to see the time and memory for it ?
Thanks.
For the time consumption, would the profiler work? It slows the execution a bit, but is nice for debugging. Otherwise try to enclose the section you want to time with tic-toc.
And for memory consumption there were, and still is I think, no really convenient way to do this, however, something may have happened here. This is how mathworks answered a few years ago. You can try whos, but that one only works inside the current scope. Also memory can be used to see matlabs total memory consumption.
The time taken for loading a file should be possible to see by enclosing it with the usual tic-toc command. The size of a saved file on disk can be seen using dir on the file, but the size could be different in matlab. I guess that the safest way is to check the size before saving if it will be loaded under the same execution and otherwise it may be convenient to log the size somehow.
Don't know if i got your question correctly, but if you need to trace the time your function takes there are two ways:
the functions
tic;
t=toc
work like a stopwatch, tic starts the counting and toc tells you how long passed since last tic.
if you need to do more in depth analysis of the times matlab also offers a profile function.
i suggest you go through matlab documentation on how to use it...
hope i helped.
S.
For execution time between code lines use:
tic;
toc;
t = toc;
disp(['Execution time: ' num2str(t)])
To know and show memory usage of variables you can use whos
whos
S = whos; % type struct variable containing all the info of the actual workspace
S.bytes
To calculate the total storage, you can make a loop
Memory = 0;
S = whos;
for k = 1:length(S)
Memory = Memory + S(k).bytes;
end
disp(['Total memory used by variables in storage (Bytes): ' num2str(Memory)])
You might prefer to see whos page in mathworks
I'm running a simulation of a diffusion-reaction equation in MATLAB, and I pre-allocate the memory for all of my vectors beforehand, however, during the loop, in which I solve a system of equations using BICG, the amount of memory that MATLAB uses is increasing.
For example:
concentration = zeros(N, iterations);
for t = 1:iterations
concentration(:,t+1) = bicg(matrix, concentration(:,t));
end
As the program runs, the amount of memory MATLAB is using increases, which seems to suggest that the matrix, concentration, is increasing in size as the program continues, even though I pre-allocated the space. Is this because the elements in the matrix are becoming doubles instead of zeros? Is there a better way to pre-allocate the memory for this matrix, so that all of the memory the program requires will be pre-allocated at the start? It would be easier for me that way, because then I would know from the start how much memory the program will require and if the simulation will crash the computer or not.
Thanks for all your help, guys. I did some searching around and didn't find an answer, so I hope I'm not repeating a question.
EDIT:
Thanks Amro and stardt for your help guys. I tried running 'memory' in MATLAB, but the interpreter said that command is not supported for my system type. I re-ran the simulation though with 'whos concentration' displayed every 10 iterations, and the allocation size of the matrix wasn't changing with time. However, I did notice that the size of the matrix was about 1.5 GB. Even though that was the case, system monitor was only showing MATLAB as using 300 MB (but it increased steadily to reach a little over 1 GB by the end of the simulation). So I'm guessing that MATLAB pre-allocated the memory just fine and there are no memory leaks, but system monitor doesn't count the memory as in use until MATLAB starts writing values to it in the loop. I don't know why that would be, as I would imagine that writing zeros would trigger the system monitor to see that memory as 'in use,' but I guess that's not the case here.
Anyway, I appreciate your help with this. I would vote both of your answers up as I found them both helpful, but I don't have enough reputation points to do that. Thanks guys!
I really doubt it's a memory leak, since most "objects" in MATLAB clean after themselves once they go out of scope. AFAIK, MATLAB does not use a GC per se, but a deterministic approach to managing memory.
Therefore I suspect the issue is more likely to be caused by memory fragmentation: when MATLAB allocates memory for a matrix, it has to be contiguous. Thus when the function is repeatedly called, creating and deleting matrices, and over time, the fragmentation becomes a noticeable problem...
One thing that might help you debug is using the undocumented: profile on -memory which will track allocation in the MATLAB profiler. Check out the monitoring tool by Joe Conti as well. Also this page has some useful information.
I am assuming that you are watching the memory usage of matlab in, for example, the task manager on windows. The memory usage is probably increasing due to the execution of bicg() and variables that have not been garbage collected after it ends. The memory allocated to the concentration matrix stays the same. You can type
whos concentration
before and after your "for" loop to see how much memory is allocated to that variable.