Sep 18 2024
Hardware

What Is Parallel Processing, or Parallelization?

Parallel processing, an integral element of modern computing, allows for more efficiency in a wide range of applications.

Modern computing has many foundational building blocks, including central processing units (CPUs), graphics processing units (GPUs) and data processing units (DPUs).

However, what almost all modern software has in common is that it can run in parallel, meaning that it can be broken down and have different tasks run multiple processing units at the same time. This enhances the efficiency of the processors and reduces computation time.

Parallelization is the process of writing software to run multiple tasks simultaneously, says Brandon Hoff, an analyst who leads networking and communications infrastructure within IDC’s Enabling Technologies team. For example, Microsoft’s Outlook email application allows users to compose email messages while simultaneously downloading incoming mail.

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What Is Parallel Processing, or Parallelization?

In parallel processing, “different parts of a computation are executed simultaneously on separate processor hardware,” says Tao B. Schardl, a postdoctoral associate in the electrical engineering and computer science department at the Massachusetts Institute of Technology.

“That separate processor hardware can be separate processor cores in a multicore central processing unit, or separate CPUs or other processing hardware in the same machine, or separate connected machines — such as a computing cluster or a supercomputer — or combinations thereof,” Schardl says.

In practice, parallel processing can be one of two things, says Jim McGregor, principal analyst at TIRIAS Research. It can be “the ability to run multiple tasks in parallel, such as multiple data processing batches or multiple instances of Microsoft Word, or breaking down a single task and running multiple portions of the task simultaneously — parallelizing — such as a neural network model.”

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How Does Parallel Processing Work?

In parallel processing, a software program is written or modified to identify what parts of the computation can be executed on separate processing hardware, Schardl says.

Those parts of the computation, or tasks, “are then run on separate processor hardware, typically using some combination of the operating system and a scheduling library,” he says.

“Parallelizing a program is often challenging, because it is generally up to the programmer writing the software program to figure out how to divide the program's computation into tasks that can be executed simultaneously, in parallel, in a correct and efficient way,” he adds.

To do this effectively, software developers need to figure out when and how the different parallel tasks must synchronize and communicate with each other, Schardl says.

A wide range of technologies — including shared memory, distributed memory and various hardware synchronization operations — exist to support synchronization and communication among tasks. These technologies provide different capabilities and impose different costs, he says.

“Programmers must figure out how to use available parallel processing hardware and communication and synchronization technologies so that tasks can execute efficiently in parallel,” Schardl says. Running various tasks in parallel also helps users avoid long wait times, which can slow down the computation overall.

If there is a software function that is relying on another process to give it data to get started, that processing is going to be serial in nature, and thus slower, Hoff says. “But if I write my code really smartly, I can make it so that there's very few of those dependencies, and I can run these things parallel,” he says.

What Are the Benefits of Parallel Processing for Organizations?

There are many benefits of parallel processing, McGregor notes, including higher overall performance, lower latency and a higher ROI for the equipment or instance being used.

Modern CPUs in computers and servers are multicore CPUs, meaning they each contain multiple parallel processor cores that can process separate tasks simultaneously.

“If you spread your workloads throughout your entire processor, then you get much better performance,” Hoff says. “If it’s single thread or only tied to one core, your performance is very limited, and your laptop or your server is sitting there doing nothing.”

“Ideally,” Schardl says, “by parallelizing a program, one could make it run P times faster, where P is the number of independent processors in the computer system. In practice, how much speed-up a program obtains from parallel processing depends on how well the software has been parallelized and engineered to use parallel processing hardware efficiently.”

GPUs achieve much of their performance advantage over CPUs by providing a large number of independent processing elements, Schardl adds. “To run faster, software must be parallelized to take advantage of these improvements in computer hardware,” he says.

LEARN MORE: How to extract the most valuable insights from your data.

What Are Some Examples of Successful Parallel Processing?

As of the mid-2000s, almost all software has been developed to run in parallel, Hoff says. However, there are some software types that especially benefit from parallel processing.

McGregor notes that those applications include artificial intelligence processing, database mining, data analytics, multimedia processing, and any kind of data modeling (financial, design or scientific).

For example, Hoff says, “I could be running a thousand queries against a database, and I just spread those thousand queries across multiple cores, and then I get better utilization out of my server.”

Another example: Using JavaScript to run web applications and display complex HTML web pages. “That runs a lot of processing, and you can parallelize all of those across your laptop for better performance,” Hoff says.

Businesses also use parallel processing in their in-house internal software “to speed up performance-critical computations that are key to their operations,” says Schardl.

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