Cluster Computing Application Based On The Finite Element Method For Electrical Prospecting

High speed network and improved microprocessor performance are making networks of computers an appealing vehicle for parallel computing. By relying solely on commodity hardware and software, network of computers can offer cost-effective high performance and high availability computing. This new wave in high performance computing is popularly called as Cluster Computing. In recent years, Cluster computing has become a hot topic research among academic and industry community.Parallel Virtual Machine (PVM) is a software package that permits a heterogeneous collection of "cluster computers' to function as a single high-performance parallel machine. Thus large computational problems can be solved more cost effectively by using the aggregate power and memory of many computers. PVM is portable and runs on a wide variety of modern platforms. With tens of thousands of users, PVM has become the de facto standard for distributed computing world-wide.This dissertation successfully uses cluster computing for solving large-scale linear algebraic equations problems when the finite element method (FEM) is applied in electrical prospecting. FEM has proven to be a versatile method for the simulation of continuous physical systems in many areas of science and engineering. But a disadvantage arises due to the very high computation times regarding conventional serial computers. FEM requires a discretization (a mesh or a model) of a domain. This thought of FEM is similar to that of cluster computing, which is divide and rule. Therefore, the finite element code can be parallelized and executed on different cluster nodes on which PVM is available. The program is written in C++, provided access to PVM through the use of calls to PVM library routines. The system of linear equations produced by the FEM is solved by conjugate gradient (CG) algorithm, which is associated naturally with finite element problems because both finite element and conjugate gradient use similar minimization procedures. The CG algorithm does not require construction of the global matrix. It can be implemented efficiently on aAbstractmassively parallel architecture.