IoT

What Is Parallel Processing? Definition, Types, and Examples

Essential to contemporary system operations, parallel processing supports multiple streams of data processing tasks through multiple CPUs working concurrently.

August 26, 2022

Parallel processing is a computing technique when multiple streams of calculations or data processing tasks co-occur through numerous central processing units (CPUs) working concurrently. This article explains how parallel processing works and examples of its application in real-world use cases. 

What Is Parallel Processing?

Parallel processing is a computing technique when multiple streams of calculations or data processing tasks co-occur through numerous central processing units (CPUs) working concurrently. 

Parallel Processing

Pictorial Representation of Parallel Processing and its Inner Workings

Parallel processing uses two or more processors or CPUs simultaneously to handle various components of a single activity. Systems can slash a program’s execution time by dividing a task’s many parts among several processors. Multi-core processors, frequently found in modern computers, and any system with more than one CPU are capable of performing parallel processing.

For improved speed, lower power consumption, and more effective handling of several activities, multi-core processors are integrated circuit (IC) chips with two or more CPUs. Most computers can have two to four cores, while others can have up to twelve. Complex operations and computations are frequently completed in parallel processing.

At the most fundamental level, the way registers are used distinguishes between parallel and serial operations. Shift registers operate serially, processing each bit one at a time, whereas registers with parallel loading process each bit of the word simultaneously. It is possible to manage parallel processing at a higher level of complexity by using a variety of functional units that perform the same or different activities simultaneously.

The interest in parallel computing began in the late 1950s, and developments in supercomputers started to appear in the 1960s and 1970s. These multiprocessors used shared memory space and carried out parallel operations on a single data set. When the Caltech Concurrent Computation project constructed a supercomputer for scientific applications using 64 Intel 8086/8087 processors in the middle of the 1980s, a new type of parallel computing was introduced.

This system demonstrated that one could attain high performance with microprocessors available off the shelf in the general market. As the ASCI Red supercomputer computer broke the threshold of one trillion floating point operations per second in 1997, these massively parallel processors (MPPs) emerged to dominate the upper end of computing. MPPs have since expanded in number and influence.

Clusters entered the market in the late 1980s and replaced MPPs for many applications. A cluster is a parallel computer comprised of numerous commercial computers linked together by a commercial network. Clusters are the workhorses of scientific computing today and dominate the data centers that drive the modern information era. Based on multi-core processors, parallel computing is becoming increasingly popular.

Parallel processing makes it possible to use regular desktop and laptop computers for solving problems that used to require a powerful supercomputer and the help of expert network and data center managers. Until the middle of the 1990s, computers made for consumers could only process data one at a time. Most operating systems today control how different processors work together. This makes parallel processing more cost-effective than serial processing in most cases.

Parallel computing is becoming critical as more Internet of Things (IoT) sensors, and endpoints need real-time data. Given how easy it is to get processors and GPUs (graphics processing units) today through cloud services, parallel processing is a vital part of any microservice rollout.

See More: What Is Ailing IoT Implementations at Scale and Ways to Fix Them

How Does Parallel Processing Work?

In general, parallel processing refers to dividing a task between at least two microprocessors. The idea is very straightforward: a computer scientist uses specialized software created for the task to break down a complex problem into its component elements. Then, they designate a specific processor for each part. To complete the entire computing problem, each processor completes its portion. The software reassembles the data to solve the complex initial challenge.

When processing is done in parallel, a big job is broken down into several smaller jobs better suited to the number, size, and type of available processing units. After the task is divided, each processor starts working on its part without talking to the others. Instead, they use software to stay in touch with each other and find out how their tasks are going.

After all the program parts have been processed, the result is a fully processed program segment. This is true whether the number of processors and tasks and processors were equal and they all finished simultaneously or one after the other.

There are two types of parallel processes: fine-grained and coarse-grained. Tasks communicate with one another numerous times per second in fine-grained parallelism to deliver results in real-time or very close to real-time. The slower speed of coarse-grained parallel processes results from their infrequent communication.

A parallel processing system can process data simultaneously to complete tasks more quickly. For instance, the system could receive the next instruction from memory as the current instruction is processed by the CPU’s arithmetic-logic unit (ALU). The main goal of parallel processing is to boost a computer’s processing power and increase throughput, or the volume of work one can do in a given time. One can use many functional units to create a parallel processing system by carrying out similar or dissimilar activities concurrently.

This is a more sophisticated way of saying that dividing the work makes things easier. You could still split the load between different processors in the same computer, or it could be split between different computers connected by a computer network. Users can accomplish the same objective in several ways.

A computer scientist typically uses a software tool to break a complex task into smaller parts and assign each portion to a processor. Each processor will then solve its part, and the data will be put back together by a software tool to read the answer or carry out the operation.

Each CPU will normally function and carry out parallel tasks as directed while reading data from the computer’s memory. Processors will also use the software to communicate and keep track of changes in data values. After a task, the software will fit all the data fragments together, assuming that all the processors stay in sync. If computers are networked to form a cluster, one can use those without multiple processors for parallel computing.

 See More: 5G vs. Fiber Optics: Which One Suits IoT Connectivity the Best?

Types of Parallel Processing

There are various varieties of parallel processing, such as MMP, SIMD, MISD, SISD, and MIMD, of which SIMD is probably the most popular. Single instruction multiple data, or SIMD, is a parallel processing type where a computer has two or more processors that all follow the same instruction set but handle distinct data types. Let us now take a look at the various kinds of parallel processing and how they work:

Types of Parallel Processing

Types of Parallel Processing

1. Single Instruction, Single Data (SISD)

In the type of computing called Single Instruction, Single Data (SISD), a single processor is responsible for simultaneously managing a single algorithm as a single data source. A computer organization having a control unit, a processing unit, and a memory unit is represented by SISD. It is similar to the current serial computer. Instructions are carried out sequentially by SISD, which may or may not be capable of parallel processing, depending on its configuration.

Sequentially carried-out instructions may cross over throughout their execution phases. There may be more than one functional unit inside a SISD computer. However, one control unit is in charge of all functional units. Such systems allow for pipeline processing or using numerous functional units to achieve parallel processing.

2. Multiple Instruction, Single Data (MISD)

Multiple processors are standard in computers that use the Multiple Instruction, Single Data (MISD) instruction set. While using several algorithms, all processors share the same input data. MISD computers can simultaneously perform many operations on the same batch of data. As expected, the number of operations is impacted by the number of processors available.

The MISD structure consists of many processing units, each operating under its instructions and over a comparable data flow. One processor’s output becomes the input for the following processor. This organization’s debut garnered little notice and wasn’t used in architecture.

3. Single Instruction, Multiple Data (SIMD)

Computers that use the Single Instruction, Multiple Data (SIMD) architecture have multiple processors that carry out identical instructions. However, each processor supplies the instructions with its unique collection of data. SIMD computers apply the same algorithm to several data sets. The SIMD architecture has numerous processing components. 

All of these components fall under the supervision of a single control unit. While processing numerous pieces of data, each processor receives the same instruction from the control unit. Multiple modules included in the shared subsystem aid in simultaneous communication with every CPU. This is further separated into organizations that use bit-slice and word-slice modes.

4. Multiple Instruction, Multiple Data (MIMD)

Multiple Instruction, Multiple Data, or MIMD, computers are characterized by the presence of multiple processors, each capable of independently accepting its instruction stream. These kinds of computers have many processors. Additionally, each CPU draws data from a different data stream. A MIMD computer is capable of running many tasks simultaneously.

Although MIMD computers are more adaptable than SIMD or MIMD computers, developing the sophisticated algorithms that power these machines is more challenging. Since all memory flows are changed from the shared data area transmitted by all processors, a MIMD computer organization incorporates interactions between the multiprocessors. 

The multiple SISD operation is equivalent to a collection of separate SISD systems if the many data streams come from various shared memories.

5. Single Program, Multiple Data (SPMD)

SPMD systems, which stand for Single Program, Multiple Data, are a subset of MIMD. Although an SPMD computer is constructed similarly to a MIMD, each of its processors is responsible for carrying out the same instructions. SPMD is a message passing programming used in distributed memory computer systems. A group of separate computers, collectively called nodes, make up a distributed memory computer.

Each node launches its application and uses send/receive routines to send and receive messages when interacting with other nodes. Systems can also use messages to provide barrier synchronization. It is possible to transfer the messages via a wide range of communication techniques, such as transmission control protocol (TCP/IP) over Ethernet and specialized high-speed interconnects, such as Supercomputer Interconnect and Myrinet.

6. Massively Parallel Processing (MPP)

A storage structure called Massively Parallel Processing (MPP) is made to manage the coordinated execution of program operations by numerous processors. With each CPU using its operating system and memory, this coordinated processing can be applied to different program sections.

As a result, MPP databases can handle enormous amounts of data and deliver analyses based on large datasets considerably faster. MPP processors typically communicate through a messaging interface and can have up to 200 or more processors working on an application. It functions by enabling the transmission of messages between processes via a set of corresponding data links.

The most common types of computers used in parallel processing systems are SIMD and MIMD. Although SISD computers can’t run in parallel on their own, a cluster can be created by connecting many of them. In a more extensive parallel system, the CPU of each computer can function as a processor. The computers work as a single supercomputer when used collectively. Grid computing is the name of this method.

See More: Distributed Computing vs. Grid Computing: 10 Key ComparisonsOpens a new window

Parallel Processing Examples

Parallel processing or parallel computing, has many important uses today. This includes:

1. Supercomputers for use in astronomy

The universe revolves slowly, and astrophysicists must use computer simulations to examine these phenomena since star collisions, galaxy mergers, and black hole ingestion can take millions of years. Such intricate models also require a lot of computing power. Recent advancements in the understanding of black holes, for instance, were made possible by a parallel supercomputer. 

Researchers have proven that the innermost portion of stuff that circles black holes before collapsing into them corresponds with those black holes, solving a four-decade-old enigma. That is crucial in assisting scientists in better comprehending the behavior of this still unknown phenomenon.

2. Making predictions in agriculture

The U.S. Department of Agriculture calculates supply and demand ratios for various essential crops each month. The projections may affect everyone, including policymakers attempting to stabilize markets and farmers trying to manage their budgets.

Researchers in the Department of Natural Resources and Environmental Sciences at the University of Illinois outperformed the federal government’s industry-standard forecast in 2018 by adding more data, such as estimates of crop growth, seasonal climate data, and satellite data. This allowed the researchers to outperform the federal government’s forecast. The petascale supercomputer, Blue Waters at the university, was responsible for parallel processing this data using machine learning algorithms.

3. Risk calculations and cryptocurrencies in banking

Most of today’s banking processes, including credit scoring, risk modeling, and fraud detection, are GPU-accelerated. The shift away from conventional CPU-driven analysis was inevitable. Around 2008, as lawmakers introduced numerous waves of post-crash financial regulations, GPU offloading reached its maturity. 

One of the early adopters was JPMorgan Chase, which said in 2011 that it was switching from CPU-only processing to hybrid GPU-CPU processing. This resulted in a 40% improvement in the accuracy at its data centers in terms of risk calculations and enabled savings of 80%. The crypto-mining frenzy, a 2019-2020 financial trend, also put GPUs in the spotlight. Without parallel processing, Bitcoin and the blockchain cannot function. The “chain” component of blockchain would disappear in without parallel computing.

4. Video post-production effects

Several high-budget film releases, such as Brad Pitt’s Ad Astra and John Wick’s intricately staged assassination operation, depend on parallel processing for post-production special effects. Blackmagic Design’s DaVinci Resolve Studio, among only a few Hollywood-standard post-production facilities with GPU-accelerated capabilities, was used in both instances. These powerful computers carry out state-of-the-art rendering depending on the ray-tracing method. 3D animation and color correction both require GPU parallel processing regularly. 

5. The American Summit computer

The American Summit is among the most notable supercomputers in the world. The device was designed by the Oak Ridge National Laboratory of the United States Department of Energy. It has a processing speed of 200 petaFLOPS, or 200 quadrillion operations per second. If every person on the planet performed one computation every second, it would take them ten months to accomplish what Summit can in only one.

The machine requires 4,000 gallons of water per minute to cool and weighs 340 tons. It is being used by scientists to better understand weather patterns, earthquakes, genetics, and physics and to create new materials that will make our lives easier.

See More: What Is Distributed Computing? Architecture Types, Key Components, and Examples 

6. Accurate medical imaging

Medical imaging was one of the first sectors to undergo a fundamental transformation due to parallel processing, particularly the GPU-for-general-computing revolution. There is currently a large body of scientific literature describing how increased computation and bandwidth capacities have resulted in considerable increases in speed and resolution for practically every area of medical imaging, including MRI, CT, X-rays, and optical tomography. 

Similar parallel-focused advancements will probably mark the next significant improvement in medical imaging, and Nvidia is leading the way. Radiologists now have better access to artificial intelligence capacities due to the company’s newly introduced parallel processing toolkit, which assists imaging systems in handling increased data and computational loads.

7. Desktops and laptops

Another example of parallel processing is Intel processors, which run most high-power modern computers. The HP Specter Folio and HP EliteBook x360’s Intel Core i5 and Core i7 CPUs each have four processing cores. The HP Z8, among the most efficient workstations in the industry, contains 56 computing cores, allowing it to handle complex 3D simulations and real-time 8K video editing.

See More: 5G vs. Fiber Optics: Which One Suits IoT Connectivity the Best?

Takeaway

While parallel processing has been around for a while, it is finding new applications in the IoT era. Modern IoT devicesOpens a new window generate vast volumes of data on a real-time basis, which one must immediately analyze to get the most relevant insights. This is why research papers such as ‘A Model-Driven Parallel Processing System for IoT Data Based on User-Defined Functions’, which will be presented at the 2020 IEEE 5th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA), seek to investigate how the two technologies can work together to unlock more value for enterprises. 

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Chiradeep BasuMallick
Chiradeep is a content marketing professional, a startup incubator, and a tech journalism specialist. He has over 11 years of experience in mainline advertising, marketing communications, corporate communications, and content marketing. He has worked with a number of global majors and Indian MNCs, and currently manages his content marketing startup based out of Kolkata, India. He writes extensively on areas such as IT, BFSI, healthcare, manufacturing, hospitality, and financial analysis & stock markets. He studied literature, has a degree in public relations and is an independent contributor for several leading publications.
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