.jpg "parallel processing")
Parallel processing is a set of techniques that enable computerized systems to perform data processing tasks simultaneously to increase the computational speed of devices.
This system creates faster execution times because at the moment an instruction is processed in a certain CPU component, another command can be read into memory, for example.
Therefore, the parallel processing system architecture is capable of improving the capacity of computers and increasing their performance over a while. With it, data can be distributed among several multiple functional units to perform different or identical operations simultaneously.
In this context, we will explain a little more about the subject, also dealing with its practical applications. Would you like to discover what parallel processing is, what are the main types and their advantages? Keep reading!
What is parallel processing?
Parallel processing is the division of a given activity into several particles, which are assigned to different CPUs to work synchronously with the assistance of technology. Therefore, it is a methodology for uniform distribution of demands with a focus on accelerating the performance of a task.
Parallel computing
This feature works on a single computer that has two or more processors, and also on a network of devices. This technique is also known as parallel computing and allows professionals to use common desktops to solve complex problems. Therefore, users no longer need the assistance of supercomputers.
Task segments
Here, tasks communicate with each other multiple times per second to produce optimized results in real-time. It divides a complete task into smaller segments, which remain in sync, and works on them simultaneously. Once a step has been completed, the next is prioritized.
Processing architecture
Parallel processing can be characterized by its architecture, composed of separate and multiple threads, homogeneous or heterogeneous, with distinct cores. All of this varies depending on the demand of the system at that moment.
It can also be symmetric, that is, it has several identical and independent processors that are managed by an operating system. This can optimize a Windows environment well, for example.
Hypertext Transfer Protocol
Actions can be coordinated through message queues or Hypertext Transfer Protocol (HTTP) or distributed with processors located at different devices on a network. Still, typically, all components in parallel processing are coupled, run on the same operating system, share a single user terminal, and perform secondary storage.
What is parallel processing for?
Parallel processing serves to reduce the total time working on a volume of data. This specific computing technique makes it possible for multiple information flows or calculation tasks to occur in parallel. It is useful for improving throughput or the amount of data processed per unit of time on a machine.
It is also essential for processing and providing more real-time data for implementing Internet of Things or IoT sensors, microservices, cloud services, graphics processing units, and different endpoints.
In parallel processing, each processor or core can use software to communicate and remain synchronized when information changes. When the process is complete, the technology that allows the execution of the instructions will bring together all the data from the different parts of the activity extracted from the memory of the computer to produce the output of the single task.
So, in complex situations or situations that require greater agility to be resolved, the resource is even more useful.
What are the types of parallel processing?
There are several types of parallel processing, which are categorized based on the instruction processing method. Each of them has different features and works differently, which is why some are more popular than others. See below the main ones and check out details about how they work!
PMP or Massively Parallel Processing
With PMP, message passing is enabled between processes with a set of related data links to make it work. Multiple processors use a messaging interface to communicate and can handle a large amount of data and different sections of the program. Each CPU uses its own memory and operating system to coordinate the execution of operations.
SIMD or Single Instruction
This type of processing is done by two or more processors that follow the same set of instructions. The CPU works differently with the data that is provided by each processor with a different set of instructions. This helps in analyzing large sets of information with similar benchmarks.
A single algorithm is applied to multiple data sets sent by multiple processing components, while all units are supervised by a central control unit. The same instructions are sent to each processor as they work on different information and the shared subsystem modules help in communicating with all other CPUS simultaneously.
MIMD or Multiple Instruction
This is the category of processing multiple data and instructions in which each set of centers executes information from different sources. So, all processor of the chain follows instructions and algorithms from the source, and the computational organization involves a parallel system capable of executing several orientations operating task flows simultaneously.
Therefore, each processor operates through a separate program, accepts individual instruction streams, and allows the management of a unique instruction stream. Data is provided by different streams and processing is done by two or more processors that use a more sophisticated algorithm and can perform multiple tasks at the same time.
MISD or Multiple Instruction
The acronym MISD refers to a single stream of data processing and multiple instructions. This method of organization includes several processing units that receive different instructions and operate similarly. Multiple processors receive the same sets of data, however, are instructed to process them differently to produce diverse results.
Initially, the developers implemented this structure solely out of theoretical interest. Each processor receives similar data input and operates according to the instructions and on an analogous data stream. The number of operations is influenced by the number of available processors and different algorithms are used to work with the information received.
SISD or Single Instruction
It is the type of processing that uses a single CPU and single data. There may be more than one functional unit, but the single control unit is responsible for all of them. On the other hand, it may or may not be able to work in parallel, depending on the configuration made by the user.
SISD processing uses multiple functional units and performs pipeline processing to perform parallel calculations. Instructions, in turn, occur in all execution phases and sequentially. The system manages a single source of data and algorithms to generate the desired results.
SPMD or Single Program
This is a MIMD variant that uses multiple data while all processors execute similar instructions. Each node interacts with other nodes, launching its application and using send and receive routines. To achieve this, processing is used as a program to pass messages through computer systems with distributed memory, aimed at a set of separate computers.
These messages can also be used for barrier synchronization. In this case, the transfer of information follows specific communication methods, such as Transmission Control Protocol or Internet Protocol (TCP/IP) or over Ethernet and dedicated interconnections of high speed, such as Supercomputer Interconnect and Myrinet.
What are the advantages of parallel processing?
Using the parallel processing methods described above in companies can bring several benefits. In addition to executing codes efficiently, they also reduce computing time and generate a more responsive work routine. See other advantages of this technique below!
- Simplifies complex data
These processing methods help professionals analyze data sets that may be very complex or have a huge volume that would be impossible to analyze sequentially. They help solve other more complex programming problems in a short period and reduce analysis costs.
A single web application can process millions of transactions every second with the help of AI and Big Data. Parallel processing, in turn, can help overcome several challenges, how to protect cyberspace or computing resources.
With this technique, several computers with multiple cores can filter much more data in real-time than conventional computers, isolated in their environments.
- Cost reduction
Implementing parallel processing helps save money in the short, medium, and long term, providing better cost per performance. Specialized professionals can use relatively cheap components to build computers with parallel processors and, therefore, increase the organization of a business's data.
The number of processors can be increased and the system configured according to your company's needs. Construction and maintenance costs are low while minimal wastage of computing power occurs. The technique allows the use of more resources to save execution time and resolve problems quickly.
- Makes sharing easier
Parallel processing methods also help you correctly manage an organization's information, and facilitate communication and data sharing between users. Furthermore, it improves storage resources, as the technique helps to optimize companies' Information Technology facilities and infrastructures.
- Improved response time
Some companies may use parallel processing to perform simulations and expand their understanding of phenomena that occur in the real world. By using multiple processors, system performance is increased and response time is improved. There is more flexible fault tolerance, as it is possible to reschedule tasks to be completed by another processor if there are errors in the system.
Everyday events do not happen one at a time nor do they end before the next one begins. To achieve this, parallel processing works to imitate real-world scenarios, considering that things occur simultaneously around us.
In this way, it can work with large volumes of information about healthcare, industries, ecosystems, traffic, agriculture, meteorological characteristics, and much more to deliver insights to managers who will make better-informed decisions.
- Effective use of hardware
One of the main advantages of parallel computing is that computers can execute code more efficiently. They save time and money by classifying data from Big Data faster than ever. Parallel processing also solves complex difficulties and offers more resources for professionals.
Processing systems improve resource utilization by distributing your workload to available hardware resources. They limit the underutilization of general equipment or specific components and improve overall machine efficiency. As a result, they reduce the time needed to obtain results and facilitate better decision-making.
- Scalability gains
A parallel processing system can be easily scaled by removing or adding new processors. In practice, this represents savings when it comes to sizing the requirements of a new project.
So, before you give a quote to a client about server costs and machine time, you will know more clearly the features that need to be implemented. Consequently, the profit margin increases.