Abstract
PRAM algorithms for Symmetric Gaussian elimination is presented. We showed actual testing operations that will be performed during Symmetric Gaussian elimination, which caused symbolic factorization to occur for sparse linear systems. The array pattern of processing elements (PE) in row major order for the specialized sparse matrix in formulated. We showed that the access function in2+jn+k contains topological properties. We also proved that cost of storage and cost of retrieval of a matrix are proportional to each other in polylogarithmic parallel time using P-RAM with a polynomial numbers of processor. We use symbolic factorization that produces a data structure, which is used to exploit the sparsity of the triangular factors. In these parallel algorithms number of multiplication/division in O(log3n), number of addition/subtraction in O(log3n) and the storage in O(log2n) may be achieved.
Highlights
BACKGROUNDIn this research we will explain the method of representing a sparse matrix in parallel by using PRAM model
We suggested testing operation for variation occurring in the elements of Symmetric Gaussian elimination
J, k are 3 dimensions and n is fixed. It gives a relationship of processing elements that meet at each vertex of a hypercube means that the algorithm can be evaluated in polylogarithmic time using a polynomial of three dimensional discrete space
Summary
BACKGROUNDIn this research we will explain the method of representing a sparse matrix in parallel by using PRAM model. We will present a parallel algorithm for sparse symmetric matrix and will be implemented to hypercube.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
