Symmetric Multiprocessing (SMP) — Characteristics and Use Cases Which statement correctly describes an aspect of symmetric multiprocessing systems?

Difficulty: Medium

Correct Answer: SMP is useful for workloads where large shared data structures must remain in memory and be processed concurrently.

Explanation:


Introduction:
Symmetric multiprocessing (SMP) places multiple identical processors under a single operating system image with shared memory. This question checks whether you can identify a true statement about SMP while avoiding common misconceptions about OS copies and contention.


Given Data / Assumptions:

  • In SMP, processors share main memory and I/O under one OS instance.
  • Workloads with shared in-memory data can benefit from parallel CPUs.
  • Shared resources require synchronization to avoid corruption.


Concept / Approach:
Validate which statement matches SMP basics: a single OS image coordinates multiple CPUs; shared memory enables parallel processing of common datasets; contention and synchronization are realities to be managed—not eliminated.


Step-by-Step Solution:
1) Reject the idea of per-CPU independent OS copies; SMP uses one OS instance.2) Recognize that shared in-memory data is a prime SMP use case—select this option.3) Statements about uniprocessor bottlenecks are too generic and not a defining property of SMP.4) Reject claims that SMP removes contention or synchronization needs—these remain important concerns.


Verification / Alternative check:
OS kernels for SMP include scheduler and locking mechanisms to coordinate cores accessing shared memory and kernel structures.


Why Other Options Are Wrong:

  • Per-CPU OS copy: Describes cluster/MPP, not SMP.
  • Uniprocessor bottlenecks statement: Not specific to SMP and overbroad.
  • No memory contention / no locks: Unrealistic; synchronization is essential in SMP.


Common Pitfalls:
Confusing SMP with distributed systems, or assuming parallelism eliminates the need for careful concurrency control.


Final Answer:
SMP is useful for workloads where large shared data structures must remain in memory and be processed concurrently.

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