Asked by Jan Simon
on 6 Dec 2011

I've found several references which tell that Matlab's `filter` is multi-threaded since R2007a, e.g. MathWorks:Solution 1-4PG4AN.

**The test:**

function myFilterTest

x = rand(1e6, 2); x1 = x(:,1); x2 = x(:,2);

% [B, A] = butter(3, 0.2, 'low'); % Butterworth 3rd order lowpass filter B = [0.01809893300751, 0.05429679902254, 0.05429679902254, 0.01809893300751]; A = [1, -1.760041880343, 1.182893262037, -0.2780599176345];

tic; for i=1:100 y = filter(B, A, x); % Matrix % clear('y'); % Avoid smart JIT interferences => same effects! end toc

tic; for i=1:100 y1 = filter(B, A, x1); % Two vectors y2 = filter(B, A, x2); % clear('y1', 'y2'); % No qualitative changes end toc

**[EDITED, 12-Dec-2012 22:38 UTC]: Explicite A and B instead of calling butter of SPT**

**Results** on a Windows7/64 Core2Duo:

Matlab 2009b/64: 5.34 sec (matrix) 5.22 sec (two vectors)

Matlab 2009b/64 started with -singleCompThread: 5.23 sec (matrix) 5.24 sec (two vectors)

Matlab 2011b: 4.75 sec (matrix) 4.99 sec (two vectors)

**My expectations:** 1. The value of a filtered signal to a specific time depends on the complete history for an IIR filter like the Butterworth. Therefore the filtering of a vector cannot take advantage from multi-threading (is this correct?). 2. In opposite to this, filtering a [n x 2] signal should occupy two cores, such that a multi-threaded `filter` should need approximately the same time as for a [n x 1] signal (is this correct?).

But my double-core processor has a load of 57% during the calculations and the filtering needs nearly the same time, when I start Matlab with the -singleCompThread flag.

**My conclusion:** It looks like ** filter is not multi-threaded**. Can somebody confirm this impression for 4 or 8 cores? Then with "x = rand(1e6, 8)" and "x1" to "x8".
I get equivalent results for FIR filter parameters with

Thanks.

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Answer by Elige Grant
on 12 Dec 2011

Accepted answer

**I ran this on my 8-core machine using R2009a:**

function myFilterTest x = rand(1e6, 8); x1 = x(:,1); x2 = x(:,2); x3 = x(:,3); x4 = x(:,4); x5 = x(:,5); x6 = x(:,6); x7 = x(:,7); x8 = x(:,8); [B, A] = butter(3, 0.2, 'low'); tic; for i=1:100 y = filter(B, A, x); % Matrix % clear('y'); % Avoid smart JIT interferences => same effects! end toc tic; for i=1:100 y1 = filter(B, A, x1); % Eight vectors y2 = filter(B, A, x2); y3 = filter(B, A, x3); y4 = filter(B, A, x4); y5 = filter(B, A, x5); y6 = filter(B, A, x6); y7 = filter(B, A, x7); y8 = filter(B, A, x8);

% clear('y1', 'y2'); % No qualitative changes end toc

clear all;

**And got this:**

Elapsed time is 16.865596 seconds. Elapsed time is 16.117599 seconds.

Only one core was active during each test.

**I ran this on my 8-core machine using R2011a and got:**

Elapsed time is 12.542615 seconds. Elapsed time is 16.268821 seconds.

All eight cores were active for the first test (on the matrix) and only a single core for the seconds test (on individual vectors).

**I added this to the bottom of the test:**

y_par = zeros(size(x)); matlabpool(8);tic; parfor j = 1:8 for i=1:100 y_par(:,j) = filter(B, A, x(:,j)); end % clear('y_par'); % No qualitative changes end toc; matlabpool close;

**And got this when using R2011a:**

Elapsed time is 13.305009 seconds. Elapsed time is 16.398203 seconds. Starting matlabpool using the 'local' configuration ... connected to 8 labs. Elapsed time is 3.542021 seconds. Sending a stop signal to all the labs ... stopped.

Show 5 older comments

Elige Grant
on 12 Dec 2011

Answer by Titus Edelhofer
on 12 Dec 2011

Hi Jan,

I gave it a try on my quadcore laptop. Using your test code using MATLAB singlethreaded or multithread indeed nearly makes no difference (in the multithreaded case the code runs with about 15% CPU in contrast to the usual 12.5% (because of hyperthreading) I see for singlethreaded code.

But: if I increase the number of columns I do see a benefit (I changed x to be rand(5e5, 20) and added a loop for the call to filter on the columns. The comparison probably isn't that fair anymore, but at least the CPU runs at about 50% ...

I contacted our development for clarification, my personal impression so far: yes, filter is multithreaded, but does not benefit as strongly as other functions do ...

Titus

Answer by Daniel
on 12 Dec 2011

As a summary answer:

The MATLAB documentation says FILTER is multi-threaded. As of r2011b, it is neither multi-threaded for Nx1 arrays nor NxM arrays. For NxM arrays a parfor loop allows for considerable speedups.

Answer by Walter Roberson
on 12 Dec 2011

Testing without butter()

R208b on Linux 64, 8 Xeon E5410 processors.

Default (maxNumCompThread is 8)

Elapsed time is 9.219355 seconds. Elapsed time is 9.215591 seconds.

maxNumCompThread = 1

Elapsed time is 9.215270 seconds. Elapsed time is 9.226053 seconds.

Really though the differences in timing are within the margin of error: my various runs had more variance than the difference between the figures I post above.

Mark Shore
on 13 Dec 2011

2011b, 64-bit Windows 7, quad core i7, 2.80 GHz.

As revised with explicit A, B.

Elapsed time is 3.507754 seconds.

Elapsed time is 3.356668 seconds.

CPU usage ~12% (one thread of eight).

Answer by Ken Atwell
on 13 Dec 2011

For me, running 11b on a dual-core MacBook Pro (i5), multi-threading kicks in only if variable x is at least 8 columns wide. Like most other reports in this post, my machine takes ~2.5 seconds per column when the column count is low. If I kick the column count up to 16, the time gets down to under 1.5 seconds per column.

Jan Simon
on 13 Dec 2011

for i=1:32; x=rand(1e6,i); tic; for j=1:10, y=filter(B,A,x); end; disp([i, toc/i]); end

2011b/64, 2 cores/Win7:

1 0.23365 Processor load: 57-64%

2 0.22441

3 0.22309

4 0.22450

5 0.22344

6 0.22479

7 0.22434

8 0.14223 <-- Processor load 95-100% !!!

9 0.14491

10 0.13942 etc, time per column stays at 0.14 sec.

For 2009a I constantly get 0.26 seconds. For 2011b -singleCompThread the times are 0.22 sec for all numbers of columns. Equivalent behaviour for RAND(1000, i).

Answer by Daniel
on 6 Dec 2011

I believe that some functions require large enough sizes (and possibly lengths equal to powers of 2) before they can benefit from multi-threading. As for the complete history part, you can implement filtering with convolution. Convolution is multiplication in the frequency domain. This means you can implement filtering based on the FFT. There are multi-threaded versions of the FFT. I believe these implementations are quite complicated and require message passing and substantial overhead. I believe that the FFT of a column vector is also multi-threaded in MATLAB.

Show 2 older comments

Jan Simon
on 6 Dec 2011

http://www.mathworks.com/matlabcentral/answers/9900-use-filter-constants-to-hard-code-filter .

The effect I've observed does *not* concern FFT.

It would be helpful, if anyone could run my code on 4, 6 or 8 cores and observe the processor load.

Daniel
on 8 Dec 2011

Jan Simon
on 8 Dec 2011

After I've upgraded to a dual core, I'm dissapointed, that e.g. the "multi-threaded" SUM is *slower* in Matlab 2009a (above the magic limit of 88999). Using -singleCompThread let SUM even be slower again. In 2011b the speed of SUM scales with the number of cores. But upgrading Matlab is expensive.

Mutli-threading the FILTER operation for a [N x 2] matrix should be trivial, because both columns can/could/should be processed separately.

The posted M-version and the C-implementation (published in the FEX) create identical results as FILTER without rounding differences. For a FFT method I'd expect at least some differences in the least signifcant bit.

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## 5 Comments

## Jan Simon (view profile)

Direct link to this comment:http://www.mathworks.com/matlabcentral/answers/23157#comment_50432

It does not concern the question, but I'm still surprised, that my naive *single threaded* C-Mex implementation of the algorithm showed in "doc filter" needs only 2.1 seconds for the above problem.

## Daniel (view profile)

Direct link to this comment:http://www.mathworks.com/matlabcentral/answers/23157#comment_50503

I wonder if the "for" loop is screwing something up... Can you turn the JIT accelerator off without turning multi threading off? My concern is that maybe the JIT accelerator realizes that it can use a parfor or some sort of multi threadingy to speed things up. Even better would be if the JIT accelerator realized that y is overwritten on every loop and therefore only the last iteration needs to be run.

## Jan Simon (view profile)

Direct link to this comment:http://www.mathworks.com/matlabcentral/answers/23157#comment_50511

@Daniel: You can insert a "clear('y')" and "clear('y1'); clear('y2')" in the loops to avoid effects of the smart JIT acceleration. But this does not change the quality of the speed. I've replaced the FOR-loop by creating an M-file, which contains 100 calls of FILTER - I still see the same timings.

The JIT cannot enable PARFOR magically, because I do not have PARFOR installed on my computer.

The processing time *and* the 57% processor load seems to show consistently, that the multi-threading of FILTER does not work properly. Therefore I'm interested in the processor load for "filter(rand(1e6, 8))" on a 8-core CPU.

## Walter Roberson (view profile)

Direct link to this comment:http://www.mathworks.com/matlabcentral/answers/23157#comment_51844

Sorry, we do not have butter() here so I cannot run the test.

## Jan Simon (view profile)

Direct link to this comment:http://www.mathworks.com/matlabcentral/answers/23157#comment_51862

I'm sorry, Walter, that I forgot to add the Signal Processing Toolbox to the "Add Products" list. Now B and A are defined explicitely.