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The Need Of Parallel Computation
With the advancement of computer science (CS), computational speed of the processors has also improved many a time. Moreover, there is certain limitation as we go upwards and face big complex problems. So we have to ready to look for alternatives. The answer denigration in parallel computing. There are two major reasons for using parallel computing: accumulate time and solve larger problems. It is clear that with the increase in amount of processors working in parallel, computation time is clear to reduce. Also, they're some scientific trouble that evens the best processor to takes months or even years to resolve. Moreover, with the application of parallelcomputing these troubles may be resolve in a few hours. Some Other reasons to accept parallel computing are:
i) Cost savings: We can use several cheap computing resources in its place of paying.
ii) It is heavily for a supercomputer.
iii) Overcoming memory constraints: one computer have very finite memory resources. For big problems, using the recollections of multiple computers may conquer this obstacle. So if we join the memory resources of multiple computers then we can simply fulfil the memory requirements of the mass problems.
iv) Limits to serial computing: Physical and practical factors both are pose significant constraints to only building ever quicker serial computers. The speed of a serial computer is totally dependent upon how quick data can travel through hardware. Total limits are the pace of light (3*108 m/sec) and the transmission boundary of copper wire (9*108 m/sec). Rising speeds necessitate increasing proximity of dealing out elements. Secondly, processor technology is allocating an increasing number of transistors to be located on a chip. However, even with atomic-level or molecular components, a limit will be reached on how little components can be made. It is increasingly costly to make a single processor quicker. Using a big number of moderately fast commodity processors to attain the same (or better) presentation is less expensive.
real world applications
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