Efficient parallel processing approach based on distributed memory systems

Complex problems need long time to be solved, with low efficiency and performance. So, to overcome these drawbacks, the approach of breaking the problem into independent parts and treating each part individually. When treating with problems that need strong processing, it is necessary to break these problems to independent parts and specify each one to a certain processor in such method that these processors can operate in parallel approach. The system that contains such processors will consist of multiple processing units connected via some interconnection network and the software needed to make the processing units work together. These systems can be Shared, Distributed or Hybrid memory systems. In this thesis, the approach of distributed memory system is depended and based on client/servers principles, the network can contain any number of nodes; one of them is a client and the others are servers. The algorithms used here are capable of calculating the (Started, Consumed, and Terminated) CPU-times, Total execution time and CPU usage of servers and Client hosts. This work addresses an improved approach for problem subdivision and design flexible algorithms to communicate efficiently between client-side and servers-side in the way to overcome the problems of hardware networking components and message passing problems. Matrix Algebra operations depended as case-study. For this case study, there are many general algorithms and other related algorithms (i.e. Network-Connection-Checking, Load-Division, Massages-Sending/Delivering, Timings-Calculating, Results-Checking, and Results-Receiving/Storing). All these algorithms designed and tested completely by this work. The obtained results are checked and monitored by special programming-checking-subroutines through many testing-iterations and proved a high degree of accuracy. All of these algorithms are implemented using Java Language. Keywords: Parallel Processing, Clustering , CPU Usage, Load Division , Parallel Computing ,CPU Timing , Distributed Memory System , Client/Servers.