MATLAB Answers

Matt J

Speed optimization of ACCUMARRAY via MEX

Asked by Matt J
on 13 Nov 2012
Latest activity Commented on by Oliver Woodford
on 20 Nov 2013

I find it strange that no FEX contributors have tried to improve upon ACCUMARRAY, since I think it's widely agreed that it is slow.

I've speculated that the reason ACCUMARRAY is so slow (despite being a built-in) is that it has to support so many different options, and was considering trying to make a customized mex version, one which supported only summation accumulation, for example.

But before I pursue that, I was wondering if there's an apriori obvious reason why it would or would not make a difference.


Matt J
on 13 Nov 2012

I guess one of the reasons for my suspicions is the difference in performance between an accumarray operation and an equivalent sparse matrix multiplication in the example below. The accumarray approach is about twice as slow as the sparse matrix approach even though they both have the same data to work with. In fact, the sparse matrix multiplication is probably even handicapped relative to accumarray, since it has to search through x for the appropriate x(J), whereas in the accumarray approach, I exclude the construction of b=x(J) from the tic/toc.

   %fake data setup
    %pretend we build A from scratch
    toc %Elapsed time is 0.062737 seconds.
    %Apply A
    toc %Elapsed time is 0.006868 seconds.
    %Using accumarray
    toc %Elapsed time is 0.012236 seconds.

Obviously though, one can't always look to a sparse matrix implementation as an alternative, because the overhead of constructing them outweighs the cost of accumarray, as the example also shows.

Matt, did you try using the 'sparse' option in accumarray?

Matt J
on 14 Nov 2012

Sean, I didn't use the issparse option because the output is not expected to be sparse.



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4 Answers

Answer by Sean de Wolski
on 13 Nov 2012
Edited by Sean de Wolski
on 13 Nov 2012

How fast do you want it?

val = rand(3000,1);
idx = ceil(rand(3000,3)*10);
t = 0;
for ii = 1:100;
  V = accumarray(idx,val,[10 10 10],@prod);

On my laptop:

ans = 0.0022

That's pretty good for accumulating 3000 values in 3d space. Bumping it up to 10000 rows it only take 0.0027 seconds...

~An accumarray fan


Answer by Mark Whirdy
on 13 Nov 2012
Edited by Mark Whirdy
on 13 Nov 2012

Hi Matt

I've never used accumarray for this reason (its very generalised so adds much overhead), and haven't yet come across a data-manipulation problem that couldn't be solved more efficiently without it (using matrix/"linear" indexing and the very handy ismember.m function [which has the mex "imembc" as its engine] ).

Can you give us an example of your specific use-case as my feeling is that pursuing a mex of accumarray will be a bit of a wild goose chase.

Best, Mark


Matt J
on 13 Nov 2012

Mark, I'm having a hard time seeing how ISMEMBER can be used to replicate the functionality of ACCUMARRAY. Could you give an example of what you mean?

Mark: The winning entry of the Color Bridge contest used accumarray 7 times. I hope the competitors would've replaced them with something faster if they knew of one. So do tell us the secret :-)

Answer by Walter Roberson
on 14 Nov 2012

I worry about the cost of the memory allocations within accumarray(). There are some operations such as the default @sum or @max that can be done as a running total, but operations such as @median have to build up each entry as a list of values, each list of unknown size. The same is true for ugly but useful constructs such as @(x) {x} that return the (in-order) list of actual values.

Does accumarray do some kind of estimation of buffer sizes and pre-allocates each, growing (with some strategy?) at need and shrink-fitting later if need be? Or does accumarray do a prepass equivalent to

accumarray(index, 1)

to count the entries that will end up at each location, use that to allocate memory, and then do the real accumarray ? In some cases, pre-allocation can be done just as

t = max(index); if any(size(t,2:end) > 1); t = [t 1]; end
array = zeros(t, class(TheData));

but accumarray doesn't know in advance that it can use that, not unless the function was defaulted, or perhaps if accumarray somehow compares the function handles to @sum, @min, @max, and other functions that can be invoked iteratively to give the scalar result.

Thus it seems plausible to me that there is room for optimization of accumarray() around issue of memory allocation. For example there could be a flag saying to invoke the function iterative (which would need also an initial value.) Or perhaps the user could pass in a buffer size which was the maximum number of elements to expect per location. Or a re-buffer size (e.g., when the end of the buffer is reached, add N more elements to the buffer.)

  1 Comment

Matt J
on 14 Nov 2012

Food for thought, Walter. Thanks!

Answer by Jan Simon
on 14 Nov 2012

The built-in command cellfun contains some procedure, which avoid to call Matlab to process a function handle. These procedure can be triggered by using strings as 1st input as explained in the documentation. These procedures are implemented in the C-Mex file of cellfun, whose source code has been shipped with older versions of Matlab.

Unfortunately these very efficient methods are marked as beeing supported for backward compatibility only now and most of the users in this forum post much slower methods using anonymous functions. I do not stop to advertise the efficient methods here. But I'm not motivated to implement an improved accumarray as Mex file, when this is not really wanted and supported for the already existing cellfun.


Matt J
on 14 Nov 2012

I think I missed the connection here between CELLFUN and ACCUMARRAY and how CELLFUN can be used to duplicate ACCUMARRAY functionality. I can see, that this


is equivalent to this

    cellfun(@sum,{[3,3], [4,4]}.')

but don't really see how you could leverage it in general. The required transformation of arrayfun arguments to cellfun arguments looks like it would impart a lot of overhead.

@Matt: I think Simon addressed "I find it strange that no FEX contributors have tried to improve upon ACCUMARRAY".

My interpretation is: by analogy, optimized calls to cellfun with first input being string would have been discarded if not for backward compatibility, thus what is the benefit of exerting effort for improving on accumarray when the general trend is to disregard speed (for clarity).

In simple words, why would I (FEX contributor) spend effort in accumarray which is already seldom used? It's up to TMW to promote certain functions, and in general certain approaches.

Jan Simon
on 14 Nov 2012

@Matt J: There is no computational equivalence between CELLFUN and ACCUMARRAY. The OP asked for a "customized mex version". As you can see in my FEX submissions, I like to create customized MEX versions. I'm using an expanded XCellFun also (e.g. with 'mean', 'sum', 'max' etc commands), but even the efficient methods built-in in Matlab CELLFUN are not loved in this forum and in CSSM. Therefore I'm not going to publish XCellFun. And I'm not going to create XAccumArray, because according to CELLFUN I'm convinced, that only very few users need or want such a tool.

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