## Matrix operations with Parallel Computing Toolbox

on 18 Jun 2013

### Shashank Prasanna (view profile)

Hi all,

for a student project, I work with the "Parallel Computing Toolbox". To discover it's functionality I make simple matrix operations with distributed matrizes.

With an open matlapool (12 workers) I use (for example) the following code, on a computer with 16 cores:

```    M1 = rand (i,i);
M2 = rand (i,i); ```
```    tic
M = M1 * M2;
toc```
```    M1 = distributed(M1);
M2 = distributed(M2);```
```    tic
M = M1 * M2;
toc```

While execution of this code I run "ksysguard" to observe the cpu-usage. During the first multiplication (with "normal" matrizes), the cpu-usage of all 16 cores is at 100%. While the second multiplication runs, the usage of 12 cores is at 100% (I think this could be correct, cause of the 12 workers). I even notice that the first multiplication is much quicker (I tested it for values of n up to 20.000), and gets quicker with bigger matrizes.

Even when I close the matlab pool and do a matrix multiplication, all 16 cores have a usage of 100%. Does this mean that matlab does parallel computation at default?

## Products

### Shashank Prasanna (view profile)

on 18 Jun 2013

Most math operations are inherently multithreaded. There is a list you will find on our external page:

http://www.mathworks.com/support/solutions/en/data/1-4PG4AN/

Jill will correct me if I am wrong, but each worker in the matlab pool is always single threaded. Which means the multithreaded operations will run single threaded on a worker but you have the advantage to scale to a large number of machines.

http://www.mathworks.com/discovery/multicore-matlab.html

### Jill Reese (view profile)

on 18 Jun 2013

A number of MATLAB functions are multithreaded, and matrix multiplication (*) is one of them. This is what you are seeing during the first multiplication (with "normal" matrices).

Sascha

### Sascha (view profile)

on 18 Jun 2013

Is there a list of the yet multithreaded functions (without matlab pool using)? So that I can figure out in which functions I can get a benefit of the Parallel Computing Toolbox.

### Sascha (view profile)

on 19 Jun 2013

#### 1 Comment

Shashank Prasanna

### Shashank Prasanna (view profile)

on 19 Jun 2013

Sascha, make sure you go ahead and accept an answer. This makes others who have questions similar to yours get their answer quickly by looking at the accepted answer.

MATLAB and Simulink resources for Arduino, LEGO, and Raspberry Pi