However, the addition operation is associative, so the order of operations does not for-loop. matter. Using this property, can compute local copies of the variable in parallel on parfor count each worker in the MATLAB pool, and then accumulate the partial results together at the end of the loop. This accumulation of a result is called a reduction operation, and the parfor count variable is called a reduction variable. To indicate that the variable is a reduction variable, count it is declared before the code block, and is used after the code block. parfor parfor A MATLAB pool is a collection of MATLAB workers that can be used to offload work from the MATLAB client interactively and in parallel. To take full advantage of to speed up parfor computations a MATLAB pool has to be open. If the MATLAB pool is not open the parfor executes sequentially in the client MATLAB session. Refer to the documentation for more details. See the Advanced Topics section of the parfor documentation for more information on the classification of variables and reduction parfor variables. Computing Pi Using Parallel-Loop Monte Carlo Method First, we verify that the MATLAB pool is open. poolSize = matlabpool('size'); if poolSize == 0 error('parallel:demo:poolClosed', ... 'This demo needs an open MATLAB pool to run.'); end fprintf('This demo is running on %d MATLABPOOL workers.\n', ... matlabpool('size')); This demo is running on 8 MATLABPOOL workers. We scale the number of by the size of the MATLAB pool to demonstrate the darts parfor weak scaling capability. parforDarts = darts * poolSize; R = 1; tic Call the parallel Monte Carlo method for computing . myPI = parallelMonteCarloPI(parforDarts,R); Measure the parallel elapsed time and display the results. parElapsedTime = toc; fprintf('The computed value of pi is %8.7f.\n',myPI); The computed value of pi is 3.1417299. Implementing the Parallel Monte Carlo Function function myPI = R)
for-loop. However, the addition operation is associative, so the order of operations does not matter. Using this property, parfor can compute local copies of the count variable in parallel on each worker in the MATLAB pool, and then accumulate the partial results together at the end of the parfor loop. This accumulation of a result is called a reduction operation, and the count variable is called a reduction variable. To indicate that the count variable is a reduction variable, it is declared before the parfor code block, and is used after the parfor code block. A MATLAB pool is a collection of MATLAB workers that can be used to offload work from the MATLAB client interactively and in parallel. To take full advantage of parfor to speed up computations a MATLAB pool has to be open. If the MATLAB pool is not open the parfor executes sequentially in the client MATLAB session. Refer to the parfor documentation for more details. See the Advanced Topics section of the parfor documentation for more information on the classification of variables and reduction variables. Computing Pi Using Parallel-Loop Monte Carlo Method First, we verify that the MATLAB pool is open. poolSize = matlabpool('size'); if poolSize == 0 error('parallel:demo:poolClosed', ... 'This demo needs an open MATLAB pool to run.'); end fprintf('This demo is running on %d MATLABPOOL workers.\n', ... matlabpool('size')); This demo is running on 8 MATLABPOOL workers. We scale the number of darts by the size of the MATLAB pool to demonstrate the parfor weak scaling capability. parforDarts = darts * poolSize; R = 1; tic Call the parallel Monte Carlo method for computing . myPI = parallelMonteCarloPI(parforDarts,R); Measure the parallel elapsed time and display the results. parElapsedTime = toc; fprintf('The computed value of pi is %8.7f.\n',myPI); The computed value of pi is 3.1417299. Implementing the Parallel Monte Carlo Function function myPI = R)
p4-11385-montecarlomethod.docx