How Parallel Computing Works

By: Jonathan Strickland  | 
A man working on a big computer
Computing power all comes down to the processor.
AP Photo/Jay LaPrete

If a computer were human, then its central processing unit (CPU) would be its brain. A CPU is a microprocessor — a computing engine on a chip. Some computational problems take years to solve even with the benefit of a powerful microprocessor, so computer scientists sometimes use a parallel computing approach called parallel processing.


What Is Parallel Computing?

Parallel computing is a broad term that involves dividing a task into smaller parts that are processed simultaneously by two or more processors. Unlike traditional sequential computing, which relies on a single processor to execute tasks one at a time, parallel computing makes use of parallel programs and multiple processing units to enhance efficiency and reduce computation time. This approach is critical for handling complex problems and large datasets in modern computing, allowing for the concurrent execution of multiple tasks.


What Does Parallel Processing Mean?

Parallel processing is a type of parallel computing. The concept is pretty simple: A computer scientist divides a complex problem into component parts using special software specifically designed for the task. They then assign each component part to a dedicated processor. Each processor solves its part of the overall computational problem. The software reassembles the data to reach the end conclusion of the original complex problem.

It's a high-tech way of saying that it's easier to get work done if you can share the load. You could divide the load up among different processors housed in the same computer or you could network several computers together and divide the load up among all of them. There are several ways to achieve the same goal.


Parallel Processing Approaches

To understand parallel processing, we need to look at the four basic programming models. Computer scientists define these models based on two factors: the number of instruction streams and the number of data streams the computer handles. Instruction streams are algorithms. An algorithm is just a series of steps designed to solve a particular problem. Data streams are information pulled from computer memory used as input values to the algorithms. The processor plugs the values from the data stream into the algorithms from the instruction stream. Then, it initiates the operation to obtain a result.

Single Instruction, Single Data (SISD) computers have one processor that handles one algorithm using one source of data at a time. The computer tackles and processes each task in order, so sometimes people use the word "sequential" to describe SISD computers. They aren't capable of performing parallel processing on their own.


Multiple Instruction, Single Data (MISD) computers have multiple processors. Each processor uses a different algorithm but uses the same shared input data. MISD computers can analyze the same set of data using several different operations at the same time. The number of operations depends upon the number of processors. There aren't many actual examples of MISD computers, partly because the problems an MISD computer can calculate are uncommon and specialized.

Single Instruction, Multiple Data (SIMD) computers have several processors that follow the same set of instructions, but each processor inputs different data into those instructions. SIMD computers run different data through the same algorithm. This can be useful for analyzing large chunks of data based on the same criteria. Many complex computational problems don't fit this model.

Multiple Instruction, Multiple Data (MIMD) computers have multiple processors, each capable of accepting its own instruction stream independently from the others. Each processor also pulls data from a separate data stream. An MIMD computer can execute several different processes at once. MIMD computers are more flexible than SIMD or MISD computers, but it's more difficult to create the complex algorithms that make these computers work. Single Program, Multiple Data (SPMD) systems are a subset of MIMDs. An SPMD computer is structured like an MIMD, but it runs the same set of instructions across all processors.

Out of these four, SIMD and MIMD computers are the most common models in parallel processing systems. While SISD computers aren't able to perform parallel processing on their own, it's possible to network several of them together into a cluster. Each computer's CPU can act as a processor in a larger parallel system. Together, the computers act like a single supercomputer. This technique has its own name: grid computing. Like MIMD computers, a grid computing system can be very flexible with the right software.


Parallel Processing Computations

Individually, each processor works the same as any other microprocessor. The processors act on instructions written in assembly language. Based on these instructions, the processors perform mathematical operations on data pulled from computer memory. The processors can also move data to a different memory location.

In a sequential system, it's not a problem if data values change as a result of a processor operation. The processor can incorporate the new value into future processes and carry on. In a parallel system, changes in values can be problematic. If multiple processors are working from the same data but the data's values change over time, the conflicting values can cause the system to falter or crash. To prevent this, many parallel processing systems use some form of messaging between processors.


Processors rely on software to send and receive messages. The software allows a processor to communicate information to other processors. By exchanging messages, processors can adjust data values and stay in sync with one another. This is important because once all processors finish their tasks, the CPU must reassemble all the individual solutions into an overall solution for the original computational problem. Think of it like a puzzle — if all the processors remain in sync, the pieces of the puzzle fit together seamlessly. If the processors aren't in sync, pieces of the puzzle might not fit together at all.

There are two major factors that can impact system performance: latency and bandwidth. Latency refers to the amount of time it takes for a processor to transmit results back to the system. It's not good if it takes the processor less time to run an algorithm than it does to transmit the resulting information back to the overall system. In such cases, a sequential computer system would be more appropriate. Bandwidth refers to how much data the processor can transmit in a specific amount of time. A good parallel processing system will have both low latency and high bandwidth.

Sometimes, parallel processing isn't faster than sequential computing. If it takes too long for the computer's CPU to reassemble all the individual parallel processor solutions, a sequential computer might be the better choice. As computer scientists refine parallel processing techniques and programmers write effective software, this might become less of an issue.


The Future of Computing With Parallel Systems

Looking ahead, the role of parallel computing in advancing technology and science is undeniable. As the complexity of computational problems grows, so does the need for more sophisticated parallel computer architectures and programming techniques. Modern computers, equipped with hardware that supports parallelism, are paving the way for innovations in everything from artificial intelligence to climate modeling. In this era, understanding and leveraging the power of parallel computing is essential for anyone looking to solve the most challenging problems of our time.

We updated this article in conjunction with AI technology, then made sure it was fact-checked and edited by a HowStuffWorks editor.


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Frequently Answered Questions

What is parallel hardware?
Parallel hardware includes the physical components, like processors and the systems that allow them to communicate, necessary for executing parallel programs. This setup enables two or more processors to work on different parts of a task simultaneously.
How do distributed memory systems differ from shared memory systems in parallel computing?
In parallel computer architectures, distributed memory systems assign separate memory areas to each processor, necessitating direct communication for data sharing. In contrast, shared memory systems provide a unified memory space accessible by all processors, easing data exchange while requiring advanced coordination to avoid data conflicts and ensure consistency.
What is cluster computing and how does it work?
Cluster computing connects multiple computers, or nodes, to act as a single entity, enhancing computational power and task management. This arrangement allows for tackling parallel tasks that are too complex for a single computer by breaking them down into smaller parts that can be processed concurrently, enhancing scalability and efficiency in dealing with large-scale computations.
Why are parallel tasks important in today’s computing landscape?
Parallel tasks are crucial for managing and analyzing big data analytics, given the computational demands of modern computers. By distributing tasks across multiple processors or computers, parallel computing enables the handling of large datasets and complex simulations more efficiently than traditional single-processor systems, allowing for quicker and more effective data analysis and problem-solving.
How does the operating system support parallel computing?
The operating system manages resources for parallel computing, allocating CPU time, memory, and other system resources to parallel tasks. It orchestrates the execution of parallel programs and ensures efficient communication between processors in a multi-tasking environment, enhancing the performance of systems with two or more processors.
What role do graphics processing units (GPUs) play in parallel computing?
Graphics Processing Units (GPUs) are crucial for enhancing parallel computing performance. With their ability to perform multiple computations simultaneously across many cores, GPUs accelerate processing tasks far beyond traditional CPU capabilities, especially in fields requiring intensive data processing like big data analytics and machine learning..