The asynchronous algorithm is thus very similar to the synchronous algorithm, except that we update as much information as possible after each design point is analyzed. The inertia is only applied when design iteration is completed. Of course, this could result in some points of the next design iteration being analyzed before the inertia operator is applied for that design iteration. However, the influence on the overall performance of the algorithm seems to be negligible [24].
IV. PROPOSED PSO & PPSO ALGORITHMS The proposed architecture is based on PSO and PPSO algorithms to calculate execution time depicted below in figure 1. Additionally, in order to evaluate the execution time for software module, a proposed PPSO (Parallel PSO) algorithm …show more content…
In PPSO, computation time of PSO can be reduced with the parallel structure. Parallel processing aims at producing the same results achievable using multiple processors with the goal of reducing the run time. The same steps described in PSO will be applied, but in step (a) PPSO will define how many group of processors needed for the cost function to be executed, because it can be designed to be 2n sets.
The performance of the Parallel PSO can be evaluated using Amdahl's Law Eq. [25].
Speedup (Sp) = 1/fs + fp/p
Where:
fs= serial fraction of code fp= parallel fraction of code P= number of processors
Suppose serial fraction of code (0.5), parallel fraction of code (0.5) and number of processors (2, 4, 8, 16, 32, 64, 128, 256, 512, and 1024).
If P=2: Sp= 1 / (0.5+0.25) = 1.33
Table 1: PPSO algorithm
P SP PPSO Elapsed Time
2 1.33 0.02
4 1.60 0.017
8 1.79 0.012
16 1.89 0.008
32 1.96 0.005
64 2.00 0.003
128 2.00 0.002
256 2.00 …show more content…
The third implementation uses PPSO where results are shown below in table 4:
Table 4: Results of PPSO x P Sp CF ET
1 2 1.33 58.73 0.02
2 4 1.60 24.48 0.017
3 8 1.79 15.00 0.012
4 16 1.89 5.85 0.008
5 32 1.96 5.85 0.005
6 64 2.00 5.85 0.003
7 128 2.00 5.34 0.002
8 256 2.00 2.73 0.001
9 512 2.00 2.73 0.001
10 1024 2.00 2.73 0.0003
In table 4 each test case (iteration) to optimize the cost function but elapsed time decreased compared with MCWA, PSO and shown below in figure 5:
Figure 5:Releationship between CF and ET in PPSO
In figure 5 shown relationships between CF and ET where found inverse relationship between them.
This paper introduces compared between PSO and PPSO where shown below in figure 6:
Figure 6:Releationship between PSO and PPSO
In figure 6 shown relationships between PSO and PPSO where elapsed time in PPSO decreased compared with PSO. In figure 7 shown relationships between SP, PSO and PPSO.
Figure 7:Releationship between SP, PSO and PPSO
In figure 7 shown inverse relationships between SP, PSO and PPSO Whenever an increase in speed occur where decreased in PSO and also more decreased in
IV. PROPOSED PSO & PPSO ALGORITHMS The proposed architecture is based on PSO and PPSO algorithms to calculate execution time depicted below in figure 1. Additionally, in order to evaluate the execution time for software module, a proposed PPSO (Parallel PSO) algorithm …show more content…
In PPSO, computation time of PSO can be reduced with the parallel structure. Parallel processing aims at producing the same results achievable using multiple processors with the goal of reducing the run time. The same steps described in PSO will be applied, but in step (a) PPSO will define how many group of processors needed for the cost function to be executed, because it can be designed to be 2n sets.
The performance of the Parallel PSO can be evaluated using Amdahl's Law Eq. [25].
Speedup (Sp) = 1/fs + fp/p
Where:
fs= serial fraction of code fp= parallel fraction of code P= number of processors
Suppose serial fraction of code (0.5), parallel fraction of code (0.5) and number of processors (2, 4, 8, 16, 32, 64, 128, 256, 512, and 1024).
If P=2: Sp= 1 / (0.5+0.25) = 1.33
Table 1: PPSO algorithm
P SP PPSO Elapsed Time
2 1.33 0.02
4 1.60 0.017
8 1.79 0.012
16 1.89 0.008
32 1.96 0.005
64 2.00 0.003
128 2.00 0.002
256 2.00 …show more content…
The third implementation uses PPSO where results are shown below in table 4:
Table 4: Results of PPSO x P Sp CF ET
1 2 1.33 58.73 0.02
2 4 1.60 24.48 0.017
3 8 1.79 15.00 0.012
4 16 1.89 5.85 0.008
5 32 1.96 5.85 0.005
6 64 2.00 5.85 0.003
7 128 2.00 5.34 0.002
8 256 2.00 2.73 0.001
9 512 2.00 2.73 0.001
10 1024 2.00 2.73 0.0003
In table 4 each test case (iteration) to optimize the cost function but elapsed time decreased compared with MCWA, PSO and shown below in figure 5:
Figure 5:Releationship between CF and ET in PPSO
In figure 5 shown relationships between CF and ET where found inverse relationship between them.
This paper introduces compared between PSO and PPSO where shown below in figure 6:
Figure 6:Releationship between PSO and PPSO
In figure 6 shown relationships between PSO and PPSO where elapsed time in PPSO decreased compared with PSO. In figure 7 shown relationships between SP, PSO and PPSO.
Figure 7:Releationship between SP, PSO and PPSO
In figure 7 shown inverse relationships between SP, PSO and PPSO Whenever an increase in speed occur where decreased in PSO and also more decreased in