Python numpy library functions to perform basic matrix operations are as follows. • C=numpy.add(A, B): Add two matrices A and B and store result in C. • C=numpy.subtract(A, B): Subtract matrix B from matrix A and store result in C. • C=numpy.divide(A, B): Divide matrix A by matrix B and store result in C. • C=numpy.multiply(A, B): Multiply matrix A by matrix B and store result in C. • C=numpy.sum(A): Form the sum of elements of matrix A and store result in c ∈ R. • C=numpy.sum(A, axis = 0): Form the column wise summation of matrix A and store result in vector C. • C=numpy.sum(A, axis = 1): Form the row wise summation of matrix A, store result in vector C. Python code to show the implementation of these methods in a sample matrix.

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Fundamentals of Multithreaded Algorithms

An algorithm that executes a single instruction in a uniprocessor system is called a serial algorithm. In the case of a multiprocessor computer system, a multiprocessor computer can execute multiple instructions in parallel. A multithreaded algorithm is an algorithm that enables multiple instances of the code to be executed concurrently.Multithreading is a concept that allows the CPU to execute threads independently while sharing similar resources. It is an important CPU feature that works on the process. Thread is a sequence of instructions that executes in parallel with other threads. Because of multithreading, multiple tasks can be performed within a single process, making the execution faster.