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Q. Explain Time Complexity in Parallel algorithms?
As it takes place nearly everyone who implement algorithms wish to know how much of an individual resource (for example time or storage) is needed for a given algorithm. The parallel architectures have been intended for improving computation power of several algorithms. So the main concern of evaluating an algorithm is the determination of amount of time needed to execute. Generally the time complexity is computed on the grounds of total number of steps implemented to achieve the desired output.
The Parallel algorithms generally divide the problem in more asymmetrical or symmetrical sub problems and shift them to various processors and put results back together at one end. The resource utilization in concurrent algorithms is both the communication overhead between the processors and also processor cycles on each processor.
Conventionally software has been written for serial computation in that programs are written for computers which have a single Central Processing Unit (CPU). Here the problems are
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How can we change almost any algorithm to have a good best case running time? Check whether the input constitutes an input at the very starting Or else run the original algo
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