What is parallel processing in computer architecture

If a central processing unit ( CPU) would be its brain. A CPU is a microprocessor -- a computing engine on a chip. While modern Computer scientists use different approaches to address this problem. One potential approach is to push for more powerful microprocessors. Usually this means finding ways to fit more transistors on a microprocessor chip. Computer engineers are already building microprocessors with transistors that are only a few dozen nanometers wide. How small is a nanometer? It's one-billionth of a meter. A red blood cell has a diameter of 2,500 nanometers -- the width of modern transistors is a fraction of that size. Building more powerful microprocessors requires an intense and expensive production process. Some computational problems take years to solve even with the benefit of a more powerful microprocessor. Partly because of these factors, computer scientists sometimes use a different approach: parallel processing. In general, parallel processing means that at least two microprocessors handle parts of an overall task. The concept is pretty simple: A computer scientist divides a complex problem into component parts using special software specifically designed for the task. He or she then assigns each component part to a dedicated processor. Each processor solves its part of the overall computational problem. The software reassembles the data to reach the end conclusion of the original complex problem. It's a high-tech way of saying that it's easier to get wo...

Parallel processing or parallel computing refers to the action of speeding up a computational task by dividing it into smaller jobs across multiple processors. Some applications for parallel processing include computational astrophysics, geoprocessing, financial risk management, video color correction and medical imaging. Believe it or not, the circuit your computer uses to render fancy graphics for video games and 3D animations is built from the same root architecture as the circuits that make possible accurate climate pattern prediction. Wild, huh? And graphic processing units’ (GPU) parallel infrastructure continues to power the most powerful computers. “If you look at the workhorses for the scientific community today, the new computers, like [IBM supercomputer] Summit, and also the next generation, like Aurora, they’re largely based on this [infrastructure] model now,” said Wen-mei Hwu, a professor of electrical and computer engineering at the University of Illinois-Urbana Champaign, who is also considered a godfather of parallel computing. Here are just a few ways parallel computing is helping improve results and solve the previously unsolvable. Parallel Processing in Aerospace and Energy When you tap the Weather Channel app on your phone to check the day’s forecast, thank parallel processing. Not because your phone is running multiple applications, but because maps of climate and weather patterns require the serious computational heft of parallel. Parallel computing ...

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Table of Contents • • • • • • • • Introduction In this increasingly advanced digital era, the need for fast and efficient computer performance is increasing. To meet these demands, computer scientists and engineers are constantly developing new technologies. One of the important concepts in improving computer performance is parallel processing. In this article, we will explore the concept of parallel processing in computer architecture, its benefits, and examples of its application in everyday life. What is Parallel Processing? Parallel processing refers to the ability of a computer to perform several tasks simultaneously. In parallel processing, the computer divides the work into smaller parts and assigns each part to a different processing unit. In this way, complex tasks can be completed more quickly as the workload is shared among multiple processors. In traditional computer architecture, computers use serial processing, which means that the computer completes one task at a time. However, with parallel processing, the computer can execute several instructions simultaneously, reducing the time needed to complete a task. Modern Computer Architecture that Supports Parallel Processing In modern computer architecture design, there are several important elements that support parallel processing. One of them is the use of multi-core processors (multi-core). A multi-core processor is a component in a computer that has several independent processing units. With a multi-core pro...

Measure of the amount of work needed to perform a computing task In granularity (or Another definition of granularity takes into account the communication If T comp is the computation time and T comm denotes the communication time, then the granularity G of a task can be calculated as: G = T c o m p T c o m m Granularity is usually measured in terms of the number of Types of parallelism [ ] Depending on the amount of work which is performed by a parallel task, parallelism can be classified into three categories: fine-grained, medium-grained and coarse-grained parallelism. Fine-grained parallelism [ ] For more, see In fine-grained parallelism, a program is broken down to a large number of small tasks. These tasks are assigned individually to many processors. The amount of work associated with a parallel task is low and the work is evenly distributed among the processors. Hence, fine-grained parallelism facilitates As each task processes less data, the number of processors required to perform the complete processing is high. This in turn, increases the communication and synchronization overhead. Fine-grained parallelism is best exploited in architectures which support fast communication. It is difficult for programmers to detect parallelism in a program, therefore, it is usually the An example of a fine-grained system (from outside the parallel computing domain) is the system of Connection Machine (CM-2) and Coarse-grained parallelism [ ] In coarse-grained parallelism, a pr...

• Courses • Online Coding Classes For Kids • Online Chess Classes For Kids • Web & Mobile App Development Course For Kids • Artificial Intelligence Coding Course For Kids • Design Course For Kids • Online Drawing & Animation Classes For Kids • Maths Course For Kids • Sample Papers • Class 4 Maths Sample Paper • Class 5 Maths Question Paper • Class 6 Maths Question Papers • Class 7 Maths Sample Paper • Class 8th Maths Sample Papers • Class 9 Maths Sample Paper • Class 10 Maths Sample Paper • Blog • Reviews • English • हिन्दी • العربية This post is also available in: हिन्दी (Hindi ) العربية (Arabic ) What Is Parallel Processing? Parallel processing can be described as a class of techniques that enables the system to achieve simultaneous data-processing tasks to increase the computational speed of a computer system. A parallel processing system can carry out simultaneous data-processing to achieve faster execution time. For instance, while an instruction is being processed in the The primary purpose of parallel processing is to enhance the computer processing capability and increase its throughput, i.e. the amount of processing that can be accomplished during a given interval of time. Country • Afghanistan 93 • Albania 355 • Algeria 213 • AmericanSamoa 1-684 • Andorra 376 • Angola 244 • Anguilla 1-264 • Antarctica 672 • Antigua&Barbuda 1-268 • Argentina 54 • Armenia 374 • Aruba 297 • Australia 61 • Austria 43 • Azerbaijan 994 • Bahamas 1-...

Image from ‍ FAQs What is Parallel Computing? Parallel computing refers to the process of breaking down larger problems into smaller, independent, often similar parts that can be executed simultaneously by multiple processors communicating via shared memory, the results of which are combined upon completion as part of an overall algorithm. The primary goal of parallel computing is to increase available computation power for faster application processing and problem solving. ‍ Parallel computing infrastructure is typically housed within a single datacenter where several processors are installed in a server rack; computation requests are distributed in small chunks by the application server that are then executed simultaneously on each server. ‍ There are generally four types of parallel computing, available from both proprietary and open source parallel computing vendors -- bit-level parallelism, instruction-level parallelism, task parallelism, or superword-level parallelism: ‍ • Bit-level parallelism: increases processor word size, which reduces the quantity of instructions the processor must execute in order to perform an operation on variables greater than the length of the word. • Instruction-level parallelism: the hardware approach works upon dynamic parallelism, in which the processor decides at run-time which instructions to execute in parallel; the software approach works upon static parallelism, in which the compiler decides which instructions to execute in paralle...