A concised discussion over relative merits & demerits of different laws to measure speed-up performance vis-à-vis to a parallel-computer algorithm-system
The Merits & demerits of different laws to measure a speed-up performances!
Amdahl’s Law
The speed-up factor help us to know the relative gain that is achieved in shifting the execution of a task from sequential computer to parallel computer and the performance does not increase linearly with the increase in number of processor.
Now, Let us consider a problem say P, which has to be solved using a parallel computer. According to Amdahl’s law, there are mainly two types of operations; therefore, the problem will have some sequential operation and some parallel operations. We already know that it requires T (1) amount of time to execute a problem using a sequential machine and sequential algorithm. The time to compute the sequential operation is a fraction ? (?<=1) of the total execution time i.e. T (1) and the time to computer the parallel operations is (1- ?), therefore S (N) can be calculated as under:-
S (N) =T (1)/T (N)
S (N) =T (1)/ (?*T (1) + (1- ?)*T (1)/N)
Dividing by T (1)
S (N) =1/ (?+ (1- ?)/N)
Remember the value of ? is between 0 and 1. Now put some values of number of processors, we find that the S (N) keeps on decreasing with increase in the value of ?.

Speed up Factor S (N) v/s ?

Outcomes of analysis of Amdahl’s Law:-
To optimize the performance of parallel computers modifies compiler need to be developed which aim to reduce the number of sequential operation pertaining to the reaction ?.
Manufacturers of parallel computers were discouraged from manufacturing large scale machine having millions of processors.
One major shortcoming identified in Amdahl’s law: according to Amdahl’s law the problem size is always fixed and of sequential operations remains mainly same.
Gustafson’s Law
There are numerous applications that require that accuracy of the resultant output should be high. In the present situation the computing power has increased substantially due to increase in number of processors attached to parallel computer. Thus it is possible to increase the size of the problem. The graph of speed up:

S (N) =? +N*(1- ?)
S (N) = N- ?*(N-1)
Thus decrease is because of overhead or sizes caused by inter processor communication.
Sun and Ni’s Law
The Sun and Ni’s Law is a generalization of Amdahl’s Law as well as Gustafson’s Law. The fundamental concept of underlying the Sunand Ni’s Law is to find the solution to a problem with a maximum size along with limited requirement of memory. Now a day, there are many applications which are bounded by the memory in contrast to the processing speed.
In a multiple based parallel computer, each processor has an independent small memory. In order to solve a problem, normally the problem is divided into sub problems and distributed to various processors. It may be noted that size of sub-problem should be in proportion with size of the independent local memory available with the processor. The size of the problem can be increased further such that the memory could be utilized. This technique assists in generating more accurate solution as the problem size has been increased.






















