Performance Analysis of Matrix-Vector Multiplication in Hybrid (MPI + OpenMP)

Computing of multiple tasks simultaneously on multiple processors is called Parallel Computing. The parallel program consists of multiple active processes simultaneously solving a given problem. Parallel computers can be roughly classified as Multi-Processor and Multi-Core. In both these classificat...

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Bibliographic Details
Published in:International journal of computer applications 2011-01, Vol.22 (5)
Main Authors: Waghmare, Vivek N, Kendre, Sandip V, Chordiya, Sanket G
Format: Article
Language:English
Subjects:
Algorithms
Chips
Computer simulation
Mathematical analysis
Mathematical models
Matrices
Matrix methods
Multiplication
Online Access:Get full text
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Summary:Computing of multiple tasks simultaneously on multiple processors is called Parallel Computing. The parallel program consists of multiple active processes simultaneously solving a given problem. Parallel computers can be roughly classified as Multi-Processor and Multi-Core. In both these classifications the hardware supports parallelism with computer node having multiple processing elements in a single machine, either in single chip pack or on more than one distinct chip respectively. Parallel programming is the ability of program to run on this infrastructure which is still quite difficult and complex task to achieve. Out of many two different approaches used in parallel environment are MPI and OpenMP, each one of them having their own merits and demerits. Hybrid model combines both approaches in the pursuit of reducing the weaknesses in individual. In proposed approach takes a pair of, Matrices produces another matrix by using Matrix-Vector Multiplication Algorithm. The resulting matrix agrees with the result of composition of the linear transformations represented by the two original matrices. This algorithm is implemented in MPI, OpenMP, and Hybrid mode. The algorithm is tested for number of nodes with different number of matrix size. The results indicates that the Hybrid approach out performs the MPI and OpenMP approach.
ISSN:0975-8887
0975-8887
DOI:10.5120/2579-3561