Comments on: Matrix processing performance

By: Python:Performance of row vs column operations in NumPy – IT Sprite

Python:Performance of row vs column operations in NumPy – IT Sprite — Thu, 05 Nov 2015 12:09:02 +0000

[…] are a few articles that show that MATLAB prefers column operations than row operations, and that depending on you lay […]

By: Peter Gardman

Peter Gardman — Fri, 05 Apr 2013 17:48:29 +0000

In reply to Yair Altman. Thank you very much Yair

By: Yair Altman

Yair Altman — Thu, 04 Apr 2013 23:44:37 +0000

In reply to Peter Gardman. @Peter - I suspect that this is due to the fact that A.B is directly interpreted by the Matlab interpreter, whereas struct(...) goes through a library routine (in libmx.dll on Windows). Note: when you place the profiled code in an m-file, rather then testing in the non-optimized Command Prompt, the speedup is only ~2 or 3, not 100 (not that a x2 speedup should be disparaged...). In a related matter, I compare alternatives for preallocating arrays of structs here.

By: Peter Gardman

Peter Gardman — Thu, 04 Apr 2013 17:13:48 +0000

Dear Yair,
I’m wondering about the fastest way of creating a structure. In my ignorance, I used to think that >>A.B = x would call A = struct(‘B’,x) so writing A = struct(‘B’,x) in an mfile would be much faster. To my surprise A.B =x is much faster:

>> tic, A.B = 3; toc
Elapsed time is 0.000003 seconds.

>> tic, M = struct(‘B’,3); toc
Elapsed time is 0.000297 seconds.

Do you have any clue why is this?, or, if you have already talked about this in a post, could you please indicate it to me where? Thank you very much

By: Waiting for asynchronous events | Undocumented Matlab

Waiting for asynchronous events | Undocumented Matlab — Wed, 18 Jul 2012 18:51:22 +0000

[…] Performance tuning can indeed be counter-intuitive sometimes, until you learn the underlying reasons when it becomes clear (I’ve shown several examples of this in the past) […]

By: Thomas M.

Thomas M. — Mon, 25 Jul 2011 16:27:35 +0000

In reply to Thomas M.. The matrix has to be that size. How can the GPU be leveraged on xPC Target? Also, what do you mean by using MEX to speed up calculations?

By: Yair Altman

Yair Altman — Tue, 19 Jul 2011 18:50:55 +0000

In reply to Thomas M.. @Thomas - You can always use a GPU or MEX to speed up the calculations, but try to think whether you really need to have all this data. Just reducing the size to (100,100) will improve performance by 2 orders of magnitude, not to mention reducing memory problems, thrashing (memory swaps) etc.

By: Thomas M.

Thomas M. — Tue, 19 Jul 2011 17:22:34 +0000

A little outside the subject, but I’ve been developing code using Simulink for xPC Target and the bottleneck in my algorithm is the large matrix calculation of A = M*N, where both M and N are (250,250). I am sampling at 0.001 and I am trying to find ways to optimize the speed. Is it possible to make this calculation faster? Also, why would this calculation take longer with xPC 5.0 compared with xPC 4.3 loaded on the same system?

By: Ajay Iyer

Ajay Iyer — Tue, 19 Jul 2011 16:46:52 +0000

Interestingly, here is a segment of code (#4) that is as fast as the #1.

% Segment #4
y=ones(1000,1000);
tic
x = x(:);
y = y(:);
for i=1:100
    y = x.*y ;
end
y = reshape(y,1000,1000);
x = reshape(x,1000,1000);
toc

By: rami

rami — Mon, 18 Jul 2011 07:01:18 +0000

The reason the second loop is faster than the third loop is that matlab uses Column-Major Order :

a = [1,2,3;4,5,6]

is stored as 1,4,2,5,3,6.

(Ref : http://en.wikipedia.org/wiki/Row-major_order#Column-major_order)

As for the first one being faster than the second one, I dont know.

Cheers