In today’s world, parallel processing is known as a great computational advance in the modern world today. Parallel processing allows you to use simultaneous processing to solve complex problems. Read this essay to know more about parallel processing, its categories, uses and so on.
Due to the development of technology and the increasing needs of modern society, computer programs and systems have achieved greater integration and complexity. In order to respond to these needs and increase efficiency, the concept of parallel processing has been proposed as a vital solution. Parallel processing means simultaneous execution of several processes by several processing resources. This provides optimization and increasing the speed of computing tasks. The simultaneous execution is done by employing multiple processing units, giving each of them a part of a bigger task. Then the task reaches the final result in much less time.
Generally, systems that contain two or more CPUs can process jobs in parallel. For a giant task, several processors are needed. Parallel processing can be done in one computer or by connecting multiple computers through a network. This method can be used not only for heavy and complex calculations, but also for solving wide and diverse problems, from analyzing large data to processing images and audio. The figure below depicts what happens in parallel processing.
Interest in parallel processing dates back to the late 1950s. Because of the developments of supercomputers at the time, the concept of parallel processing gained attention. Supercomputers were multiple shared memory processors that operated next to each other on shared calculations and data. A new type of parallel processing was launched in the mid-1980s. This system showed that by using microprocessors, it is possible to achieve great performance in high speeds. These parallel processors broke the barrier of one trillion floating point operations per second (1 teraFLOPS) with the ASCI Red computer in 1997. After that, MPPs (Massively Parallel Processing) continued to grow in size and power. In 1980s, a group of computers called a cluster competed and eventually replaced MPPs for many applications. A cluster is a type of parallel computer made up of a large number of off-the-shelf computers that are connected by an off-the-shelf network. Today, clusters are the engine of scientific computing and the dominant architecture in the data centers that lead the modern information age. Today, parallel processing based on multi-core processors is becoming mainstream. Currently, most desktop and laptop systems are readily available with dual-core and quad-core processors. Manufacturers increase overall processing performance by adding more CPU cores. The reason for this is that increasing performance through parallel processing is far more energy-efficient than increasing the microprocessor’s clock frequency. In a world that is highly fast-paced and developing, efficient processing has become essential.
speed: Parallel prcessing plays a key role in solving complex problems. It can break complex problems into smaller tasks and execute the individual tasks at the same time by the multiple processors which allows computers to work in much more speeds.
Productivity: Computers capable of parallel processing can make better use of resources to process and solve problems. Today, most of them are equipped with hardware that includes multiple cores, threads, or processors, allowing them to run several processes simultaneously. Parallel processing results in more productivity.
Enhanced Scalability: Parallel processing systems can be equipped by adding more processors or nodes to the system or even removing them based on need. This adaptation allows for processing workloads without reducing performance.
Cost-effectiveness: Purposeful hardware architectures allow both parallel processing and saving money. A parallel programming hardware system may require more components than a serial processing system, but it is more efficient at performing tasks. This means parallel processing produces more results in less time than serial programs and have more financial value over time.
Fault resilience: If one processor or node fails in parallel processing, other parts of the system continues processing and do not let the whole system fail. In serial processing system, if one of the components loses function, all the system fails.
Although parallel processing has many benefits, it also has limitations. They are:
Coding requirement: Parallel processing needs some codes that may be difficult for programmers learning them. The complexity of coding these computers can be a challenge for users and they will need more advanced processing systems.
Non-parallelizable tasks: some tasks can not be run in parallel because some parts are run after the final result of the previous part. Such dependencies on other parts make parallel processing less efficient in terms of time consumption.
Maintenance: The coding of parallel processing systems may require constant updates and adjustments to maintain the performance. Due to the specialized functions of many of these computers, the maintenance of the systems often overshadow benefits.
Complexity: For a parallel processing system, communication and coordination between processors is significantly important. The complexity of a parallel processing system is effective for handling difficult tasks. However, simple systems usually suffice small tasks.
Additional Hardware: Parallel processing systems needs more hardware, such as specialized hardware accelerators or multiple processors.
Parallel processing is used all over the world in a wide range of applications. As technology in many fields developed, powerful processing has become essential for more researches and developments. Some of the common applications are as stated.
HPC (High-Performance Computing): Over time, complexities of natural phenomena and algorithm made us develop processing systems. HPC as an advanced and fast computing system operates by a large number of processors working in parallel. Today, HPC has an essential role in many fields such as physics, engineering, and other scientific works.
ML & AI: Training neural networks to process big datasets and complex computations and data mining are of examples of Machine Learining and Artificial Intelligence which make parallelism necessary for increasing perfrmance.
Scientific calculation: Climate Modelling, weather forecasting, molecular simulations, etc. need a powerful processing system to be achieved. Basically, big problems such as processing massive amounts of data, simulation and prediction of climate changes and large-scale researches in chemistry all need high-performance computing systems working in parallel.
Editing and Rendering Videos: Using parallelism, Editing and rendering high-quality visual produtcs like videos and images can be perfectly done. Especially, those products with many small details.
Video games: In recent years, gaming indusrty has grown rapidly due to the use of parallel processing. Better environment simulation, more-real interaction of objects, rendering complex parts, etc. are the most considerable improvements that effectively boosted physics and graphics of the video games.
Cryptography: Parallel processing by high-performance computing systems have directly improved the performance of cryptographic operations such as encryption, decryption, block-chain mining, and hashing, processing large-scale data.
Financial Simulation: Financial risks can be analyzed and simulated in high speeds providing that the available system process problems in parallel. These simulations and analysis often require many random calculations.
Genetic studies: Parallelism helps processing enourmous amount of genetic data focused on sequencing techniques and analysis of genetic variations, while the computations are less time-consuming.
Database control: Parallel search in a massively large database results in easier access to all files.
Sometimes, you may need a system to process in parallel to do your occasional projects. In this case, buying a required systemc is not a good solution because after use, it is not needed anymore. Thus, Renting a supercomputer as a parallel processing system would be a great idea to deal with the projects. By renting, you can benefit a powerful system that fulfill your needs with a reasonable price.
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