Parallel

High-Performance Computing for Scientific Discovery High-performance computing (HPC) lets scientists test ideas at a scale no single workstation can match. By combining thousands of cores, fast memory, and powerful accelerators, HPC turns detailed models into practical tools. Researchers can run many simulations, analyze vast data sets, and explore new theories in less time. A modern HPC system merges CPUs, GPUs, large memory, and fast interconnects. The software stack includes job schedulers to manage work, parallel programming models such as MPI and OpenMP, and GPU libraries for acceleration (CUDA, HIP, OpenCL). The result is a flexible platform where units of work can scale from a laptop to a national facility. ...

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: ...