Parallel Programming: Definition, Benefits and Industry Uses

By Indeed Editorial Team

Published July 21, 2021

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.

With the increasing use of digital technology for business, it's important for business owners and professionals to understand common computer processes. One important computer function is parallel programming. Learning about what parallel programming is and how it works can help you apply this feature effectively in your industry. In this article, we define what parallel programming is, describe how it works, explain its benefits and industry uses and provide a few examples to help you understand its diverse applications in different industries.

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What is parallel programming?

Parallel programming is a programming model that allows a computer to use multiple resources simultaneously to solve computational problems. While earlier versions of software programs followed a serial process, meaning they could only direct their resources to solve one problem at a time, parallel programming allows computers to process several problems at the same time. Most modern computers use this kind of programming, and it has extensive uses in various industries.

Industries that use parallel programming

Many industries apply parallel programming to perform various functions. Diverse industries, including the sciences, engineering, research, industrial, commercial and retail fields, implement parallel computing programs to solve problems, processes data, create models and produce financial forecasts. In addition to industry uses, many personal computers also use this kind of programming to support everyday functions like running search engines or hosting video conferencing software. Some other examples of parallel processing uses in the real world include:

  • Tracking, processing and storing big data

  • Biomedical engineering

  • Pharmaceutical design

  • Economic forecasting

  • Collaborative digital workspaces

  • Supporting multimedia sharing

  • Artificial intelligence, virtual reality and advanced graphics

  • Online search engines

  • Medical imaging and diagnostics

  • Logistical planning and tracking for transportation

  • Weather prediction

The widespread applications of parallel programming make it an increasingly essential function of modern computers.

Benefits of parallel programming

Here are the primary benefits of this type of programming:

Efficiency

A computer that uses parallel programming can make better use of its resources to process and solve problems. Most modern computers have hardware that includes multiple cores, threads or processors that allow them to run many processes at once and maximize their computing potential. When computers use all their resources to solve a problem or process information, they are more efficient at performing tasks.

Cost-effectiveness

Additionally, the hardware architecture that allows for parallel programming is more cost-effective than systems that only allow for serial processing. Although a parallel programming hardware system may require more parts than a serial processing system, they are more efficient at performing tasks. This means that they produce more results in less time than serial programs and hold more financial value over time.

Speed

Another benefit of parallel computing is its ability to solve complex problems. Parallel programs can divide complex problems down into smaller tasks and process these individual tasks simultaneously. By separating larger computational problems into smaller tasks and processing them at the same time, parallel processing allows computers to run faster.

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Limitations of parallel processing

Although parallel processing has many advantages, it also has limitations. Some of these limitations include:

  • Coding requirements: Learning to write parallel processing codes may be more challenging for programmers, but the complexity of coding these computers can be an exciting challenge for programmers and leads to sophisticated processing systems.

  • Maintenance needs: The coding of parallel processing systems may need more frequent updates and adjustments to maintain the quality of its performance. However, because of the specialized functions of many of these computers, their maintenance requirements are often less significant than the advantages of using them.

  • Complexity: Serial systems may work better in some instances since they demand less communication and coordination between processors. The complexity of a parallel processing system is effective for handling complex tasks, while simple systems are usually sufficient for simple tasks.

Approaches to parallel processing

There are four different computer architectures that support parallel processing. Computer scientists define these models based on how they implement two factors: instruction streams and data streams. An instruction stream is an algorithm, which is a sequence of instructions that programs use to solve problems. A data stream is the information that a computer pulls from its memory storage. Computers use the algorithms provided by their instruction stream to process the data from their data stream and complete tasks.

Here are the four different computer models and how they use instruction and data streams for parallel processing:

Single instruction, single data (SISD)

This type of computer architecture on its own works as a sequential computer. It uses one processor and can only handle one algorithm with one data stream at a time. Since this computer can only perform one process at a time, it's not capable of performing parallel computing unless it's connected to another computer. A user can connect several SISD computers together in a network to perform parallel processing.

Many conventional personal computers still use SISD architecture. Since these computers often perform basic functions like connecting to the internet and running word processing software, they may not need the more advanced processing abilities of a specialized parallel processing computer. However, it's becoming more common for personal computers to have more complicated architectures that allow parallel processing. This is because technological advances have expanded the functions that modern computer users expect from their devices. For example, streaming videos, hosting virtual conferences and playing video games on the computer works better with a more advanced processing system.

Multiple instruction, single data (MISD)

An MISD computer has multiple processors, and each works with a different algorithm. However, all of the processors for this computer use the same shared data stream. MISD computers can use different processors to perform multiple different computations of the same data at the same time. The number of computations it can perform at a time depends on the number of processors it contains.

These computers are relatively uncommon, but some industries might use them for highly specialized purposes. For example, aerospace engineers might use this type of architecture for a computer that manages the flight controls of space shuttles. In this application, the MISD computer processes the same set of data in multiple ways to create a fail-safe system that prevents computer errors, ensuring the controls always remain operational.

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Single instruction, multiple data (SIMD)

SIMD models use multiple processors and multiple data streams but the same algorithm across each processor. This type of computer uses the same instructions to process different sets of data to obtain a result. A SIMD model can be useful for analyzing large data sets using the set of criteria but may have more limited applications for handling complex computational problems.

Some applications for this computer architecture include 3D modeling, image processing, speech recognition, video and sound applications and networking. Many modern computers include SIMD architecture for multimedia processing. These computers can run more complex processes than SISD computers, allowing them to host more vivid graphics, produce better sound quality, and stream videos and video conferencing software with fewer interruptions.

Multiple instruction, multiple data (MIMD)

A MIMD system uses multiple processors to run different instruction streams with input from different sets of data. Each processor in a MIMD can function independently from the others, which allows this type of architecture to run several processes at the same time. Although MIMD computers have more flexibility than SIMD or MISD systems, their complexity makes them more challenging to create and maintain.

Applications for this type of computer include computer-aided design and manufacturing, simulations, modeling and as communication switches, which is a device that connects other devices together in a network. Industries that use these computers most often include engineering and research. Scientists can use these computers to create models and process complex sets of data. For example, a meteorologist might use this kind of computer to track the development of a hurricane and create forecasts with varying degrees of probability to predict how strong the storm may become and what areas it might affect.

Types of parallel programming models

There are two broad classifications for parallel programming models:

Process interaction

Process interaction refers to the mechanisms that allow parallel processes to communicate with each other. The three most common forms of interaction are:

  • Shared memory: Shared memory is a model in which parallel processes share equal access to the computer's data storage space. Since all processes in the computer's system have the same level of access to reading and writing stored data, this model facilitates the efficient exchange of information between processes.

  • Message passing: A message passing system allows parallel processes to exchange information by passing messages to each other, supporting their ability to work together to complete tasks. These messages can either be synchronous (being sent and delivered in real time) or asynchronous (delivered and interpreted at different times).

  • Implicit interaction: Implicit interaction is a characteristic of a computer's programming that makes it inherently parallel. A programmer might write a computer program using implicitly parallel code which provides the necessary structure for a program to communicate within itself while performing parallel processing.

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Problem decomposition

Since parallel computing works by separating larger tasks into smaller processes and running them concurrently, they require the ability to decompose problems. Here are three processes that allow parallel programming to decompose problems:

  • Task parallelism: Task parallelism allows a computer to distribute tasks between its processors. It works by running several tasks at the same time using the same data, which emphasizes communication between processors.

  • Data parallelism: Similarly to task parallelism, data parallelism works through distribution. However, this function differs from task parallelism by distributing data between processors rather than using the same data across all processes.

  • Implicit parallelism: Computer programmers implement both implicit parallelism and implicit interaction through writing implicitly parallel code. Like with implicit interaction, implicit parallelism is the foundational coding structure that enables the computer to run parallel processes.

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