What Is Parallel Processing? (With Types and FAQs)

By Indeed Editorial Team

Published June 6, 2022

The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.

You can use different methods to analyze data computation problems and provide solutions within a company. It's essential to use an effective data processing method to help you improve data-driven decision-making and increase daily productivity. Understanding how to use multiprocessing systems in a computer architecture can help you efficiently analyze large and complex datasets and generate accurate results.

In this article, we discuss parallel processing, outline the different types, explore its hardware architecture, review its benefits and disadvantages, provide helpful tips to help you implement it in computer architecture, and answer some frequently asked questions.

What is parallel processing?

Parallel processing or multiprocessing refers to a computing method that helps process large tasks by separating them into multiple parts and completing them simultaneously with two or more central processing units (CPU). This type of processing helps improve performance and reduces the time for completing a task. You can use any operating system with multiple CPUs, such as multi-core processors, to perform multiprocessing methods.

Types of multiprocessing

You may divide parallel processors into four groups based on data streams and instructions. These groups include:

SISD computer organization

SISD means single instruction and single data stream. This computer organization includes a processing unit, control unit, and memory unit. SISD, like a serial computer, executes instructions sequentially and may perform multiprocessing functions. In this method, instructions carried out sequentially may overlap during the execution stages. In addition, a SISD computer may have more than one functional unit, but these units function under the administration of one control unit. You can execute multiprocessing in these systems using several functional units or pipeline processing.

SIMD computer organization

SIMD refers to single instruction and multiple data streams. This organization method includes several processing elements functioning under the administration of a single control unit. In this system, the processors receive similar instructions from the control unit but execute them on several data items. In addition, the shared subsystem has numerous modules to help you communicate with the processors simultaneously. You can further divide the SIMD system into bit-scale and word slice mode organizations.

MISD computer organization

MISD means multiple instructions and single data stream. This organization method includes several processing units receiving different instructions and operating over a similar data flow. In this structure, the output of one processor becomes the input of another processor. It's important to note that developers initially implemented this structure for theoretical interest.

MIMD computer organization

MIMD refers to multiple instructions and multiple data streams. This computer organization involves the processors in the parallel system executing different instructions and operating on various data simultaneously. In MIMD computer organization, each processor operates on a separate program, and you can generate a unique instruction stream for each program.

Types of multiprocessing hardware architecture

The major multiprocessing hardware architecture in the server market include:

Symmetric multiprocessing (SMP)

This architecture's a single device with several processors managed by a single operating system with access to a similar memory area and disk. An SMP typically has eight to 32 processors, a large memory, a parallel database, good design, and a good disk. This type of architecture usually performs well with a medium-sized warehouse. It's important that the database runs its processes in parallel, and the data warehouse design can take advantage of these parallel capabilities.

Generally, the processors can quickly access shared resources, but the access path may become a bottleneck due to scalability issues. The SMP machine is a single entity. As a result, it may become a single point of failure in the warehouse. To solve this problem, hardware companies developed techniques that allow you to link multiple SMP machines to each other.

Massively parallel processing (MPP)

These systems include different independent computers with separate disks, operating systems, and memory coordinated by sharing information with each other. This system is relatively fast and efficiently provides solutions to problems. The major advantage of this system is the capability to link hundreds of machine nodes and use them to solve any issue by applying the brute-force approach. For instance, suppose you want to perform a full scan of a larger table. In that case, applying a 100-node MPP system allows each node to scan 1/100th of the table.

Non-uniform memory architecture (NUMA)

The non-uniform memory architecture is a set of MPP and SMP. It attempts to combine the parallel speed of MPP and the shared disk adaptability of SMP. This innovation is novel and may be suitable for high-run data warehousing. This architecture is conceptually similar to SMP clustering machines but includes greater coordination among nodes, more bandwidth, and tighter connections. You can consider using the NUMA architecture if you can divide the data warehouse into independent groups and put each group on its node.

Benefits of multiprocessing

Here are some of the benefits of using multiprocessing methods in your workplace:

  • Supports multiprocessors: This type of processing allows you to use multiprocessors or different processors connected through a network.

  • Executes code efficiently: Multiprocessing is an efficient means of executing codes and also helps reduce the computing time.

  • Solves larger programming issues: Multiprocessing can help you resolve larger programming issues in a short period.

  • Simplifies complex or large data: This processing method helps you analyze data sets that may be too complex or large that it may be impractical to analyze them sequentially.

  • Reduces data analysis costs: Implementing multiprocessing helps you save costs in the long run by giving you a better cost per performance. You can also build multiprocessing computers from relatively cheap components.

  • Increases data organization: This processing method also helps you properly organize the organization's data and makes communication and data sharing easier.

  • Enhances data storage capabilities: Multiprocessing helps you optimize the company's data storage facilities.

  • Real-world application: Unlike sequential processing, you can use multiprocessing for simulating, understanding, and modelling real-world phenomena.

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Disadvantages of multiprocessing

Here are some challenges to note before creating parallel systems in your workplace:

  • Complex parallel structures: Writing programming to target parallel structures may be challenging due to the complex nature of parallel structures.

  • Increased costs: You may incur extra costs due to synchronization, data transfers, thread creation or destruction, and communication. For instance, multi-core processors may require huge power to function effectively, increasing electricity costs.

  • Code adjustments for various target architectures: The parallel system may require you to perform different code tweaking to improve the performance in different target architectures.

  • Data clusters may require additional cooling: The parallel system may require better cooling technologies for your data clusters.

  • Long debugging and implementation times: The solutions in parallel systems may be harder to prove correct, debug, or implement and may not perform optimally due to coordination and communication overhead.

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Tips for implementing an effective multiprocessing architecture

Here are some tips to help you mitigate some of the challenges associated with multiprocessing:

  • Understand the problem: It's essential to identify and understand the issues before adding data, including hardware, or implementing algorithms.

  • Use good software developing practices: It's essential to use established software developing practices, such as using simple and consistent codes throughout the development process, continuous testing, and reviewing new updates.

  • Analyze operating systems: You may also analyze the different operating systems to help you understand how the code may function in different environments.

  • Consider serial implementation: It may be beneficial for you to use sequential processing for short-running parallel programs.

  • Determine the scalability: It's advisable to determine the algorithm's stability to help solve scalability problems if you want to work with varying-sized databases.

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Frequently asked questions about multiprocessing

Here are some common questions related to multiprocessing:

What are the different types of parallelism?

The different types of parallelism in computer architecture include:

  • Functional parallelism: This type of parallelism arises from the logic of a problem solution. It typically occurs in formal descriptions of solutions like dataflow graphs, program flow diagrams, and programs.

  • Data parallelism: This type of parallelism occurs in restricted problem sets such as image processing and engineering or scientific calculations. In addition, data parallelism provides growth to the large parallel execution for the data-parallel element.

What are the differences between multiprocessing and parallel computing?

Although some IT professionals may use both multiprocessing methods and parallel computing together, there're some distinguishable differences between both processes. For example, multiprocessing focuses on the central processing units and the number of cores running parallel to execute a task. In contrast, parallel computing focuses on how the behaviour of the software when computing different data streams simultaneously.

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What are the differences between multiprocessing and serial processing?

Unlike multiprocessing, where you can execute different tasks simultaneously, serial processing allows you to execute a single task at a time. Serial processing, also called sequential processing, uses only one processor and typically executes tasks in the manner and order you originally input them. In addition, sequential processing may take more time to complete tasks than multiprocessing methods.

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