Performance evaluation and comparison of MPI and PVM using a cluster based parallel computing architecture

Parallel computing operates on the principle that large problems can often be divided into smaller ones, which are then solved concurrently to save time (wall clock time) by taking advantage of non-local resources and overcoming memory constraints. The main aim is to form a common cluster based parallel computing architecture for both MPI and PVM, which demonstrates the performance gain and losses achieved through parallel processing using MPI and PVM as separate cases. This can be realized by implementing the parallel applications like solving matrix multiplication problem, using MPI and PVM separately. The common architecture for MPI and PVM is based on the Master-Slave computing paradigm. The master will monitor the progress and be able to report the time taken to solve the problem, taking into account the time spent in breaking the problem into sub-tasks and combining the results along with the communication delays. The slaves are capable of accepting sub problems from the master and finding the solution and sending back to the master. We aim to evaluate and compare these statistics of both the cases to decide which among MPI and PVM gives faster performance and also compare with the time taken to solve the same problem in serial execution to demonstrate communication overhead involved in parallel computation. The results with runs on different number of nodes are compared to evaluate the efficiency of both MPI and PVM. We also show the performance dependency of parallel and serial computation, on RAM.