parallelprocessing

Parallel processing is a computing approach in which multiple processing elements execute tasks simultaneously to increase performance or capacity. It relies on dividing a program or a dataset into parts that can be processed concurrently. Two common forms are data parallelism, where the same operation is applied to many data items in parallel, and task parallelism, where different tasks run concurrently on separate data or resources.

Architectures include shared-memory systems, in which processors access a common memory space, and distributed-memory systems, in which each processor has private memory and communicates via a network. Hardware examples range from multi core and many core CPUs and GPUs to compute clusters and supercomputers. Specialized accelerators such as GPUs and tensor processing units are optimized for highly parallel workloads.

Programming models and frameworks enable parallelism. Shared-memory models include OpenMP and threading libraries. Distributed-memory models rely on message passing with MPI. GPU programming often uses CUDA or OpenCL. Higher level data processing frameworks such as MapReduce, Apache Spark, and Dask provide abstractions for parallel data analysis.

Performance is governed by speedup, efficiency, and overheads from synchronization, communication, and data movement. Amdahl's law sets an upper bound on speedup for fixed problem size, while Gustafson's law emphasizes scaling the problem size to maintain efficiency on large parallel systems.

Applications span scientific computing, simulations, graphics rendering, machine learning training and inference, big data analytics, and financial modelling. Parallel processing continues to evolve with architectural innovations and programming models that aim to exploit increasing core counts and accelerator hardware.