Parallel computing executes many operations simultaneously across multiple processing units to reduce wall-clock time for workloads that can be decomposed into independent or loosely coupled parts.
In Plain Terms
- Splitting a big job into pieces and working on many of them at the same time across several processors, so the whole thing finishes far faster — like a team each tackling part of a task at once instead of one person doing it all in sequence.
Semantic Classification
Content
- Parallel computing covers data parallelism, task parallelism and pipeline parallelism, mapped onto multicore processors, vector units and many-core accelerators. Performance is governed by how well a problem partitions and by communication and synchronisation overheads.
- Real-time rendering depends on massive data parallelism across the GPU, where the same shader runs over millions of pixels or vertices at once. Frameworks such as CUDA expose this parallelism for both graphics and general compute used in physics and neural rendering.