Final answer:
Parallelism in shared-memory architectures using OPENMP is limited by the available number of processors or cores. Programming using MPI allows for parallelism across nodes in a distributed-memory system,
Step-by-step explanation:
Parallelism is a technique used to divide a computational problem into smaller subproblems that can be solved concurrently, utilizing multiple processors or cores. In shared-memory architectures using OPENMP, the limit on how much parallelism can be exploited depends on the available number of processors or cores. However, programming using MPI (Message Passing Interface) can help solve this problem by allowing parallelism across multiple nodes in a distributed-memory system.
In shared-memory architectures, parallelism can be expressed using compiler directives, such as #pragma omp parallel, which creates multiple threads that can execute different tasks concurrently. The actual level of parallelism achieved depends on the number of available threads and hardware resources. However, the maximum achievable parallelism is ultimately limited by the number of processors or cores.
On the other hand, MPI is a library that enables communication and coordination between multiple processes running on different nodes in a distributed-memory system. It allows for parallelism across nodes, which means that the problem can be split among multiple machines, each responsible for solving a portion of the problem. This greatly enhances the potential parallelism that can be exploited.