

Articles
by
Hamed Al Rjoub 
Total Records (
3 ) for
Hamed Al Rjoub 





Hamed Al Rjoub
,
Arwa Zabian
and
Ahmad Odat


Sorting is one of the most important operations in database systems and its efficiency can influences drastically the overall system performance. To accelerate the performance of database systems, parallelism is applied to the execution of the data administration operations. We propose a new deterministic Parallel Sorting Algorithm (DPSA) that improves the performance of Quick sort in sorting an array of size n. where we use p Processor Elements (PE) that work in parallel to sort a matrix r*c where r is the number of rows r = 3 and c is the number of columns c = n/3. The simulation results show that the performance of the proposed algorithm DPSA out performs Quick sort when it works sequentially. 




Hamed Al Rjoub


Problem statement: To calculate sensitivity functions for a large dimension control system using one processor, it takes huge time to find the unknowns vectors for a linear system, which represents the mathematical model of the physical control system. This study is an attempt to solve the same problem in parallel to reduce the time factor needed and increase the efficiency. 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 parallel algorithm divided 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}×x_{i}') can be calculated in parallel by multiplication unknowns X_{i}×X_{i}', where i = 0,1,…n1. Results: The running time t was reduced to O(t/n1) and, The Performance of parallel algorithm was increased by 4055%. Conclusion: Used parallel algorithm reduced the time to calculate sensitivity function for a large dimension control system and the performance was increased. 




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. 





