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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 n-1 processors where n is a number of linear equations which can be represented as TX = W, where T is a matrix of size n1xn2, 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 (n-1) processors to find the vector of unknowns for original system x = (x1,x2,…,xn)T and in parallel using (n-1) processors to find the vector of unknowns for similar system (x')t = dtT-1 = (x1',x2',…xn')T by using Net-Processors, where d is a constant vector. Finally, sensitivity function (with respect to variation of component ∂X/∂hi = (xi×xi') can be calculated in parallel by multiplication unknowns Xi×Xi', where i = 0,1,…n-1. Results: The running time t was reduced to O(t/n-1) and, The Performance of parallel algorithm was increased by 40-55%. 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 Al-Sha`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 n-1 processors where n is a number of linear equations which can be represented as TX = W, where T is a matrix of size n1xn2, X = T-1W, 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 (n-1) processors to find the vector of unknowns for original system x = (x1,x2,....,xn)T and in parallel using (n-1) processors to find the vector of unknowns for similar system (x`)t = dtT-1 = (x1`,x2`,....xn`)T by using Net-Processors, where d is a constant vector. Finally, sensitivity function (with respect to variation of component ∂X/∂hi = (xixxi`) can be calculated in parallel by multiplication unknowns Xix Xi`, where i = 0,1,....n-1. Results: The running time t is reduced to O(t/n-1) and, the performance of (PSME) was increased by 30-40%. Conclusion: Hence, used (PSME) algorithm reduced the time to calculate sensitivity function for a large dimension control system and the performance was increased.
 
 
 
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