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Mohsen

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13 Jun 2013 Worker Object Wrapper Simplifies managing resources such as large data within PARFOR loops and SPMD blocks Author: Parallel Computing Team

Thanks for the nice file!

However, I have tried to use it in my code in the following way and it resulted in an increase of the simulation running time compared to the non-parallelized version of the code.

I am dealing with large arrays and data files and have to calculate some statistics for a very large number of different cases.

In the initial non-parallelized version of my code, I calculate the statistics for case 1 to case 10^7 inside a for loop.

Non-parallelized Code:

... "read many files"
... "generate large arrays"

for i:1=10^7
... "Calculate statistics"
end

... "Write statistics in a text file"

In the parallelized version of the code, I use a PARFOR loop. However, I cannot have all the codes to calculate the statistics directly inside the PARFOR loop due to Matlab restrictions and errors. So, I had to create a new function called STAT and copy all the codes to calculate the statistics in this function.

Parallelized Code:

... "read many files"
... "generate large arrays"

w1=WorkerObjWrapper(Large_Array1);
w2=WorkerObjWrapper(Large_Array2);
w3=WorkerObjWrapper(Large_Array3);

parfor i:1=10^7

STAT(w1.Value, w2.Value, w3.Value, arg1, arg2, arg3, arg4);
..."slice STAT"

end

... "Write statistics in a text file"

The problem with my parallelized version of the code is that it takes much longer than the non-parallelized version. Inside the PARFOR loop, I have to call a function (STAT) and pass many large arrays (w1.Value) at each iteration.

Does anyone know what is the best way to optimize/parallelize this code?

Many thanks!

19 Jul 2011 Attractors Nonlinear systems generating attractors. Author: Giampiero Campa

Hi,

I am modelling population growth using Lotka-Volterra competitive equations (not the predatory - prey ones).

Did you use these equations in a model as well?

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