Parallel Computing Questions Medium
Shared memory and distributed memory parallel computing are two different approaches to achieving parallelism in computing.
Shared memory parallel computing refers to a model where multiple processors or cores share a common memory space. In this approach, all processors have direct access to the same memory, allowing them to communicate and synchronize with each other by reading and writing to shared variables. This shared memory can be accessed by any processor at any time, making it easier to share data and coordinate tasks between processors. Examples of shared memory parallel computing architectures include symmetric multiprocessing (SMP) systems and multicore processors.
On the other hand, distributed memory parallel computing involves multiple processors or computing nodes that have their own private memory. These processors are connected through a network and communicate with each other by passing messages. Each processor operates independently and has its own memory space, which means that data needs to be explicitly transferred between processors when needed. Distributed memory parallel computing is typically used in high-performance computing (HPC) systems and clusters, where each node can have its own processor and memory. Examples of distributed memory parallel computing architectures include message passing interface (MPI) and cluster computing.
In summary, the main difference between shared memory and distributed memory parallel computing lies in how processors access and share memory. Shared memory parallel computing allows processors to directly access a common memory space, while distributed memory parallel computing involves processors with their own private memory that communicate through message passing.