Parallel Computing vs Distributed Computing: Concepts, Principles, and Differences

Parallel computing, also known as parallel processing, allows multiple instructions to be executed simultaneously, using either time parallelism (multiple pipelines) or space parallelism (multiple processors), aiming to accelerate problem solving and increase problem scale.

Accelerate solution speed.

Increase problem size.

The principle of parallel computing involves dividing work into independent parts, executing multiple instructions concurrently, and returning results to the host for final processing.

Typical steps include: (1) separating tasks into discrete independent parts; (2) executing multiple program instructions simultaneously; (3) aggregating processed results for output.

Basic requirements for parallel computing are: (1) a parallel computer with at least two processors connected via a network; (2) applications that can be decomposed into parallelizable sub‑tasks; (3) a parallel programming environment to implement and run parallel algorithms.

Distributed computing splits a large problem into many small parts and distributes them across multiple computers, possibly across a network, then combines the results. It enables resource sharing and load balancing.

Shared rare resources.

Load balancing across multiple machines.

Running programs on the most suitable computers.

Hadoop is an early distributed computing framework developed by the Apache Foundation, allowing users to write distributed programs without needing to understand low‑level details.

In conclusion, parallel computing, distributed computing, grid computing, and cloud computing all belong to the high‑performance computing (HPC) domain, primarily aimed at large‑scale data analysis and processing, each with distinct principles and application scenarios.