Parallel processing is a computational technique in which multiple processors or cores work simultaneously on different parts of a problem or task, significantly speeding up the overall execution time. This method is widely used in environments where tasks can be broken down into smaller, independent sub-tasks that can be executed concurrently.

Parallel processing can be applied across various levels:

Instruction-level parallelism, where multiple instructions are executed simultaneously within a single processor.
Thread-level parallelism, where multiple threads execute on different cores of the same processor.
Task-level parallelism, where different tasks run on separate processors in distributed computing environments.
This approach is commonly used in high-performance computing (HPC), scientific simulations, machine learning, and data processing workloads. By leveraging parallel processing, organizations can process large datasets or perform complex calculations more efficiently than with sequential processing.

Frameworks like Hadoop, Apache Spark, and CUDA for GPU computing allow developers to harness the power of parallel processing for big data analytics and deep learning tasks. Multithreading and distributed computing are also critical components in modern parallel processing strategies.