Parallel processing

Parallel processing is a method of simultaneously breaking up and running program tasks on multiple microprocessors in order speed up performance time. Parallel processing may be accomplished with a single computer that has two or more processors (CPUs) or with multiple computer processors connected over a computer network. Parallel processing may also be referred to as parallel computing.

Parallel processing allows individuals — as well as network and data center managers — to use ordinary desktop and laptop computers to solve complex problems that once required the assistance of a powerful supercomputer. Until the mid-1990s, consumer-grade computers could only process data serially. Today, most operating systems manage how multiple processors work together, making parallel processing a more cost-effective alternative to serial processing in most scenarios.

The importance of parallel computing continues to grow along with an increasing need for real-time results by Internet of Things (IoT) endpoints. Today's easy access to processors and graphic processor units (GPU) through cloud services makes parallel processing an important consideration for any microservice rollout.

Parallel processing runs two or more task segments simultaneously on multiple processors to reduce the overall time for processing massive volumes of data.

How Parallel Processing Works

In parallel processing, a complex task is split into multiple smaller tasks that are more proportionate in processing requirements, size, and number to the processing units on hand. After the division, each processor starts working on their part of the task independently from one another, with the exception of constant communication through the software in order to stay up to date regarding the state of other tasks.

After the processing of all the parts is complete—whether the number of processors was the same as the number of tasks and they ended in sync or took turns—the result is a fully-processed program segment. Parallel process can be characterized as being either fine-grained or coarse-grained. In fine-grained parallelism, tasks communicate with each other several times per second to provide results in real or near-real time. Coarse-grained parallel processes are slower because they communicate less often.

Parallel Processing Architectures

• Multicore — a device's integrated circuit (IC) has two or more separate processing cores, each of which can execute program instructions in parallel. Multi-core architectures can be homogeneous and have identical cores, or heterogeneous and have cores that are not identical.
• Symmetric — two or more independent, homogeneous processors are controlled by a single operating system instance that treats all processors equally.
• Distributed — processors are located on different networked devices that communicate and coordinate actions through HTTP or message queues. ‍
• Massively parallel computing — numerous computer processors simultaneously execute a set of computations in parallel.
• Loosely Coupled Multiprocessing — individual processors are configured with their own memory and are capable of executing some user and operating system instructions independently of each other.

Types of Parallel Processing

Currently, there are mainly three types of parallel processing, categorized according to the source of the data and the type of processing it goes through.

• Multiple Instruction Multiple Data (MIMD) Processing– each set of processors runs data coming from different sources, following instructions and algorithms from the data source.
• Multiple Instruction Single Data (MISD) Processing – multiple processors receive the same data sets but are instructed to process them differently to produce more diverse results.
• Single Instruction Multiple Data (SIMD) Processing – multiple processors run the identical task using the same instructions to validate results.

Parallel vs Serial vs Concurrent Processing

Parallel processing and concurrent processing are often confused with one another because they both work on processing multiple tasks at the same time. Concurrent processing is similar to real-life multitasking, but tasks don't need to be completed at the same time.

Current and Future Applications of Parallel Processing

Parallel processing has a role in countless achievements ranging from scientific discoveries, like building complex computer models to map out how mass orbits around a black hole, to predictions that aid the economy. In 2019, researchers at the University of Illinois used parallel to help the US Department of Agriculture more accurately predict yield-boosting crop traits by incorporating more data than before and processing it in record times.

Parallel processing plays a major role in developing and implementing machine learning algorithms and AI programs because it allows them to run faster, process more data points and generate more accurate (and useful) insights.