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In the context of parallel processing, a problem involving data coupling can occur when multiple parallel processes or threads share data. Data coupling refers to the degree to which different modules or components of a system depend on each other's data.
One specific problem that can arise is known as "inter-thread or inter-process communication bottleneck." This occurs when parallel processes or threads need to communicate frequently or share large amounts of data. If the communication mechanism between these processes or threads is not efficiently implemented, it can lead to bottlenecks and degrade performance.
For example, if multiple threads need to access and update a shared data structure concurrently, without proper synchronization mechanisms in place, it can result in data inconsistency, race conditions, and contention for resources. This can lead to degraded performance and scalability issues in parallel processing systems. Therefore, managing data coupling effectively is crucial for optimizing parallel processing performance.
