use the Process object of the multiprocessing module to make Python implement row-partitioning for parallel matrix multiplication. Each of the three processes receives its row and matrix B from the master process and multiplies them to create the row of the output matrix C. The input matrices A and B are tiny 33 matrices. A worker then sends its related row of matrixC to the master. For this algorithm, we must take the following into account: The data of the parent process is entirely present in the process when it is launched using the Process module, but any changes it makes are not reflected in the parent process. In order to have the product matrix C shared between the processes, we need to use the Manager object of the multiprocessing module and define C as an array to be shared from this object. Each worker process performs multiplication and stores the result in its row entry of matrix C.
use the Process object of the multiprocessing module to make Python implement row-partitioning for parallel matrix multiplication. Each of the three processes receives its row and matrix B from the master process and multiplies them to create the row of the output matrix C. The input matrices A and B are tiny 33 matrices. A worker then sends its related row of matrixC to the master. For this
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