Definition: Performing Multiple Computations Simultaneously - 8.1.2 | Module 8: Introduction to Parallel Processing | Computer Architecture
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8.1.2 - Definition: Performing Multiple Computations Simultaneously

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Introduction & Overview

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Quick Overview

**Parallel processing** is a computing paradigm where a large problem or multiple smaller problems are broken into tasks and executed **concurrently (at the same physical time)** on different processing units. It differs from **concurrency**, which implies multiple computations making progress over time (possibly interleaved on a single processor), whereas parallelism requires true simultaneous execution on distinct resources. ### Medium Summary At its core, **parallel processing** is a computing approach that involves breaking down a single large problem, or managing several independent problems, into smaller, more manageable sub-problems or tasks. The defining characteristic is that these individual tasks are then executed **simultaneously** on distinct processing units or different components within a single unit. The key idea is to move beyond sequential execution (one instruction after another) to allow multiple instruction sequences or multiple instances of the same instruction to operate on different data pieces at the same time, thereby accelerating overall computation. It's crucial to distinguish this from **concurrency**, which allows multiple computations to make progress over the same period (often via interleaving on one processor), while true parallelism strictly means **simultaneous execution** on physically separate resources. ### Detailed Summary ### Definition: Performing Multiple Computations Simultaneously At its core, parallel processing is a computing paradigm where a single, large problem or multiple independent problems are broken down into smaller, manageable sub-problems or tasks. These individual tasks are then executed concurrently (at the same physical time) on different processing units or different components within a single processing unit. * **Key Idea**: Instead of executing a sequence of instructions one after another (sequentially), parallel processing allows multiple instruction sequences, or multiple instances of the same instruction, to operate on different pieces of data simultaneously. This concurrent execution is what fundamentally accelerates the overall computation. * **Contrast with Concurrency**: It's important to distinguish parallel processing from concurrency. Concurrency refers to the ability of multiple computations to make progress over the same period, often by interleaving their execution on a single processor (e.g., time-sharing in an OS). Parallelism means true simultaneous execution on physically distinct processing resources. While often intertwined, a concurrent system doesn't necessarily need parallelism, but a parallel system is inherently concurrent.

Standard

At its core, parallel processing is a computing approach that involves breaking down a single large problem, or managing several independent problems, into smaller, more manageable sub-problems or tasks. The defining characteristic is that these individual tasks are then executed simultaneously on distinct processing units or different components within a single unit. The key idea is to move beyond sequential execution (one instruction after another) to allow multiple instruction sequences or multiple instances of the same instruction to operate on different data pieces at the same time, thereby accelerating overall computation. It's crucial to distinguish this from concurrency, which allows multiple computations to make progress over the same period (often via interleaving on one processor), while true parallelism strictly means simultaneous execution on physically separate resources.

Detailed Summary

Definition: Performing Multiple Computations Simultaneously

At its core, parallel processing is a computing paradigm where a single, large problem or multiple independent problems are broken down into smaller, manageable sub-problems or tasks. These individual tasks are then executed concurrently (at the same physical time) on different processing units or different components within a single processing unit.
* Key Idea: Instead of executing a sequence of instructions one after another (sequentially), parallel processing allows multiple instruction sequences, or multiple instances of the same instruction, to operate on different pieces of data simultaneously. This concurrent execution is what fundamentally accelerates the overall computation.
* Contrast with Concurrency: It's important to distinguish parallel processing from concurrency. Concurrency refers to the ability of multiple computations to make progress over the same period, often by interleaving their execution on a single processor (e.g., time-sharing in an OS). Parallelism means true simultaneous execution on physically distinct processing resources. While often intertwined, a concurrent system doesn't necessarily need parallelism, but a parallel system is inherently concurrent.

Detailed

Definition: Performing Multiple Computations Simultaneously

At its core, parallel processing is a computing paradigm where a single, large problem or multiple independent problems are broken down into smaller, manageable sub-problems or tasks. These individual tasks are then executed concurrently (at the same physical time) on different processing units or different components within a single processing unit.
* Key Idea: Instead of executing a sequence of instructions one after another (sequentially), parallel processing allows multiple instruction sequences, or multiple instances of the same instruction, to operate on different pieces of data simultaneously. This concurrent execution is what fundamentally accelerates the overall computation.
* Contrast with Concurrency: It's important to distinguish parallel processing from concurrency. Concurrency refers to the ability of multiple computations to make progress over the same period, often by interleaving their execution on a single processor (e.g., time-sharing in an OS). Parallelism means true simultaneous execution on physically distinct processing resources. While often intertwined, a concurrent system doesn't necessarily need parallelism, but a parallel system is inherently concurrent.

Definitions & Key Concepts

Learn essential terms and foundational ideas that form the basis of the topic.

Key Concepts

  • Parallel processing involves breaking problems into tasks and executing them simultaneously on multiple processing units.

  • Its key idea is the acceleration achieved by concurrently operating on different parts of data or different instruction sequences.

  • Parallelism strictly implies true simultaneous execution on physically distinct resources.

  • Concurrency refers to computations making progress over the same period, which can be achieved through interleaving on a single processor.

  • A parallel system is inherently concurrent, but a concurrent system does not necessarily require parallelism.