Distributedmemory

Distributed memory is a computer architecture in which each processing unit has its own private memory. There is no single global address space that all processors can access directly. Instead, data sharing between processors is achieved through explicit interprocess communication, typically by sending and receiving messages. This model emphasizes data locality: computation runs on local memory, and remote data must be retrieved via communication over a network.

In distributed memory systems, processors are commonly organized as clusters of nodes connected by a network interconnect. Each node may itself be a multi-core or multi-processor machine with its own fast internal memory. Performance depends on the latency and bandwidth of the network, the topology of connections, and the efficiency of the communication library. Programs often follow data-parallel or task-parallel patterns and rely on message-passing to coordinate work and exchange results.

Programming models and tools for distributed memory include the Message Passing Interface (MPI) as the de facto standard for explicit data movement. Other models such as Unified Parallel C (UPC), Chapel, X10, and Coarray Fortran provide higher-level abstractions for shared or partitioned data across distributed memory. Within a node, shared-memory techniques (for example, OpenMP) may be combined with distributed memory approaches in hybrid configurations.

A related concept is distributed shared memory (DSM), which aims to present a global address space while still using distributed memory underneath. DSM can simplify programming but introduces overhead and potential coherence challenges.

Distributed memory systems are widely used in high-performance computing, large-scale simulations, and data analytics due to their scalability and cost-effectiveness, though they require careful programming to manage data locality, synchronization, and communication overhead.