When parallelizing code for multiple nodes, OpenMP and MPI (Message Passing Interface) are two commonly used approaches, but they differ in their underlying concepts and methodologies.

OpenMP (Shared Memory)

OpenMP is a programming model primarily used for shared memory parallelization. It allows you to parallelize code within a single node or multi-core processor by dividing the workload among multiple threads. However, OpenMP does not inherently support parallelization across multiple nodes.

When using OpenMP, a single instance of the program is executed on a node, and multiple threads are created to distribute the workload across available cores. Each thread can access shared memory, enabling easy communication and synchronization between threads. However, when working with multiple nodes, OpenMP alone cannot handle communication between nodes.

MPI (Distributed Memory)

MPI, on the other hand, is a message passing library used for distributed memory parallelization. It allows you to parallelize code across multiple nodes in a cluster or a distributed computing environment. Each node operates independently and has its own memory. MPI provides functions for communication and coordination between nodes.

With MPI, you launch multiple instances of the program, called MPI processes or ranks, each running on a separate node. These processes communicate by explicitly sending and receiving messages through MPI function calls. Data is exchanged using point-to-point communication or collective operations. MPI supports a wide range of communication patterns, allowing efficient coordination between nodes.

When allocating multiple nodes

OpenMP: When using OpenMP, you typically allocate multiple cores within a single node. OpenMP itself does not handle node allocation directly. The operating system or a resource manager (e.g., SLURM, PBS) is responsible for assigning cores to your OpenMP threads.

MPI: With MPI, you allocate multiple nodes explicitly. You launch multiple MPI processes, each running on a separate node. The resource manager or a specific MPI launcher (e.g., mpirun) handles the node allocation for you.

In summary, OpenMP is suitable for shared memory parallelization within a single node, while MPI is designed for distributed memory parallelization across multiple nodes. MPI provides explicit control over inter-node communication, whereas OpenMP focuses on intra-node parallelism.