Memory Optimization using Dynamic Programming: A Comprehensive

Abstract: Dynamic Programming (DP) is one of the main techniques applied in problem solving by breaking a complex problem into its small, easier parts. It focuses on how these parallel methods are applied in current parallel computing systems, especially shared memory systems using OpenMP and distributed memory systems using MPI. We analyzed and compared execution times, scalability, and communication costs in which these parallel DP methods perform well. Shared memory systems are easier to implement for small to medium problems because they have low communication costs. Distributed memory systems are better suited for large problems though distributed memory systems have higher communication costs. This gives one an idea of the strengths and weaknesses associated with different parallel DP methods. It also enables the choice of the most relevant methods according to the features of the computing problem