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# Matrix multiplication is one of the applications that can benefit much from the multithreading approach of parallelism. Data distribution is an important implementation detail that, if abstracted out of a module interface, can facilitate code reuse, each of which can be given to a separate independent thread. The multiplications of rows and columns can be divided over different thread workers, all of which can be joined together to make up the result matrix. # Create a thread that works on multiplying one Row of the first matrix (A) with every column of the second matrix (B) and generate one row in the resulted matrix (C).
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Matrix multiplication is one of the applications that can benefit much from the multithreading approach of parallelism. Data distribution is an important implementation detail that, if abstracted out of a module interface, can facilitate code reuse, each of which can be given to a separate independent thread. The multiplications of rows and columns can be divided over different thread workers, all of which can be joined together to make up the result matrix.
Create a thread that works on multiplying one Row of the first matrix (A) with every column of the second matrix (B) and generate one row in the resulted matrix (C).