OpenMP

Parallel and Distributed Computing Fundamentals Parallel and distributed computing help apps run faster and handle larger data. Parallel means splitting work to run parts at the same time on one machine. Distributed uses several machines that cooperate over a network. Both aim to speed up tasks, but they require different designs, communication patterns, and error handling. Key ideas to keep in mind: Concurrency and parallelism are related but not the same. Concurrency is about managing many tasks; parallelism is about doing them at the same time. Granularity matters. Fine granularity uses many small tasks; coarse granularity uses fewer larger tasks. Synchronization helps avoid conflicts, but can slow things down. Common tools include locks, barriers, and atomic operations. Communication latency and bandwidth shape performance. Shared memory is fast but limited to one machine; message passing scales across machines. Fault tolerance matters. In distributed setups, failures are expected and must be detected and handled gracefully. Models and patterns provide common solutions: ...