Computer Science – 15.1 Processors, Parallel Processing and Virtual Machines | e-Consult

Programming massively parallel computers presents several significant challenges. Data distribution is a major concern. Deciding how to divide the input data amongst the processors is critical for efficient parallel execution. Poor data distribution can lead to load imbalance, where some processors are idle while others are overloaded.

Communication overhead is another key challenge. Processors need to exchange data frequently, and this communication can be a bottleneck. The cost of message passing (e.g., network latency, serialization/deserialization) can significantly impact overall performance. Minimizing communication is a primary design goal.

Synchronization is essential to ensure that processors coordinate their activities and maintain data consistency. Synchronization mechanisms (e.g., locks, barriers) can introduce overhead and contention, potentially limiting parallelism. Incorrect synchronization can lead to race conditions and incorrect results. Furthermore, debugging parallel programs is considerably more complex than debugging sequential programs due to the non-deterministic nature of parallel execution. Tools for parallel debugging are often required.