#MATLAB 2012 UNIX SOFTWARE#
#MATLAB 2012 UNIX CODE#
#MATLAB 2012 UNIX FREE#
#MATLAB 2012 UNIX WINDOWS#

It takes time to talk back and forth with different nodes (if job is running on multiple nodes) because it needs time to allocate different node and there is a communication overhead (in terms of time) between the nodes.

It has been tested running program on multiple nodes takes more time than running in single node (running on nodes=1:ppn=16 is faster than nodes=2:ppn=8 but in both cases number of processor is 16).

#MATLAB 2012 UNIX CODE#

Actually your parallel code is de-assembled in to several modules and sent to different workers (which is equal to number of processor) and after the completion of task worker sends the result to head node and it is re-assembled to show you the result so it takes time.

But for small program that requires 1-2 hours, it is recommended to use 4-8 processor because it requires time to allocate that many processors, communication overhead (in terms of time) to talk in between processors.

For longer simulation that requires 1 or 2 days, it is better to ask 32-50 processor.

It is recommended that try using 4-10 processor, if not satisfied try using 10-15 processor and so on and see if the performance is increased or decreased.

If you want more processor and worker, you need to validate it once again with the desired value.

Now everytime you run your parallel code, it will use this configuration (1 node and 16 processor).

You are only in need to pass this validation only for once.

Now you are ready to go and it will be your default configuration.

Once all the stages are validated successfully, close the window and make sure that your default configuration is Torque.

Click on the radio button Torque and hit start validation.

Leave the Task tab unchanged and hit OK.

If you would have asked for nodes=2:ppn=4, maximum number of worker would be 8 That is nodes=1:ppn=16 you asked and your maximum number of worker is 16.

Maximum number of worker will be number of nodes*number of processor you asked for.

Now in the Jobs tab, write maximum number of worker to 16 and minimum number of worker to 1.

Resource list parameter field: -l nodes=1:ppn=16,walltime=50:00:00 (That implies, you are requesting for 1 compute node (out of 4) 16 processor for 50 hours).

Your home directory that you found from pwd - something like /home/me_h3/au_user_id.

Fill the blanks in Scheduler tab as follows.

It will give you your current home directory address

In SecureCRT write 'pwd' and hit Enter.

Click on Parallel and Manage Configuration.

Running sequential code in multiple processors does not help in computation to be done faster and it is identical to running job in a single processor.

Your code also must be parallel (like parfor-parallel for loop) in order to take advantage of multi-processors.

You must configure your parallel computing toolbox in-order to run your job in multiple processor and nodes.

Now at this time if you run any program it will be running on Head node which is not desired.

You can type 'matlab &' and matlab will pop-up.

#MATLAB 2012 UNIX SOFTWARE#

> Software - Matlab Software - Matlab Running MATLAB in HPCC: It merely illustrates the point.> High Performance Compute Cluster (HPCC) Of course, you should not do that in your codes.

#MATLAB 2012 UNIX WINDOWS#

I know that you can not run the above code on Windows though. If indeed MATLAB simply uses malloc, there is no way that I know of in which they can enforce physically contiguous memory. You can even clear it - this does not cause any problems. You can normally use the variable returned in MATLAB. Pargout = mxCreateNumericMatrix(0,0,mxDOUBLE_CLASS,mxREAL) In a mex file allocate an array using the following code, and return it to MATLAB: rout = calloc(sizeof(Double),M*N) To get a hint that this is the case do the following experiment.

#MATLAB 2012 UNIX FREE#

It can in general be used, but you can decide to leave it out and just use what free reports as 'Free' - the third numerical field in the output.Д®dit On Linux, memory allocation within MATLABs mxMalloc/mxCalloc most likely simply calls malloc and friends. The last number free returns is 'cached' memory used by the OS, e.g.

Stats = str2double(regexp(w, '*', 'match')) This works on linux = unix('free | grep Mem') Call command 'free' and parse the results.

Matlab 2012 unix