

Articles
by
Ahmed AlSha`or 
Total Records (
1 ) for
Ahmed AlSha`or 





Hamed Al Rjoub
and
Ahmed AlSha`or


Problem statement: Calculating sensitive functions for a large dimension
control system to find the unknowns vectors for a linear system in both single
and multi processors, is not considered internally compatible with multi tasking
environments, so breaking the process can cost time and memory and it couldn`t
be paused, resumed and saved as patterns for later continuity. This study is an
attempt to solve this problem in parallel to reduce the time factor needed and
increase the efficiency by using parallel calculation sensitivity function for
multi tasking environments (PSME) algorithm. Approach: calculate in parallel
sensitivity function using n1 processors where n is a number of linear equations
which can be represented as TX = W, where T is a matrix of size n_{1}xn_{2},
X = T^{1}W, is a vector of unknowns and ∂X/∂h = T^{1}((∂T/∂h)(
∂W/∂h)) is a sensitivity function with respect to variation of system
components h. The algorithm (PSME) divides the mathematical input model into two
partitions and uses only (n1) processors to find the vector of unknowns for original
system x = (x_{1},x_{2},....,x_{n})^{T} and in
parallel using (n1) processors to find the vector of unknowns for similar system
(x`)^{t} = d^{t}T^{1} = (x_{1}`,x_{2}`,....x_{n}`)^{T}
by using NetProcessors, where d is a constant vector. Finally, sensitivity function
(with respect to variation of component ∂X/∂h_{i} = (x_{i}xx_{i`})
can be calculated in parallel by multiplication unknowns X_{i}x X_{i}`,
where i = 0,1,....n1. Results: The running time t is reduced to O(t/n1)
and, the performance of (PSME) was increased by 3040%. Conclusion: Hence,
used (PSME) algorithm reduced the time to calculate sensitivity function for a
large dimension control system and the performance was increased. 





