Physical organization of Parallel Platforms

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The physical organization of parallel platforms refers to the way in which the processors, memory, and other components are physically connected and arranged in the system. There are several different physical configurations that can be used to build parallel platforms, each with its own advantages and disadvantages.

Here are some common physical organization configurations for parallel platforms:

  1. Symmetric multiprocessing (SMP):
  • All processors share a common memory.
  • Processors are typically connected through a bus or a switch.
  • Memory is accessed through a memory controller that manages access requests from all processors.
  • Advantages include low latency and ease of programming.
  • Disadvantages include scalability limitations due to contention for shared resources.
  1. Non-uniform memory access (NUMA):
  • Similar to SMP, but with multiple memory controllers to reduce contention.
  • Each processor has its own local memory and can access remote memory through a memory controller.
  • Advantages include improved scalability and reduced contention for shared resources.
  • Disadvantages include higher complexity and cost compared to SMP.
  1. Cluster:
  • Multiple nodes or computers connected through a network.
  • Each node may have its own processors and memory.
  • Communication between nodes is typically through message-passing.
  • Advantages include scalability, fault tolerance, and the ability to handle large-scale problems.
  • Disadvantages include high latency and complexity in managing communication.
  1. Hybrid:
  • A combination of SMP and NUMA or cluster architectures.
  • Can provide a balance between performance, scalability, and ease of programming.
  • Examples include clusters of SMP or NUMA nodes, or SMP systems with specialized coprocessors such as GPUs.

Overall, the choice of physical organization depends on the specific requirements of the application, the number of processors involved, and other factors. Each configuration has its own trade-offs in terms of performance, scalability, cost, and ease of programming.

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