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What is Parallel Computing?

Parallel Computing
4 min read

Whether you’re familiar with parallel computing or not, fully understanding its value and potential is important.

Parallel computing is a term that is frequently used in the software industry. Its presence has, indeed, been felt in a variety of other industries as well. What exactly does this type of computing architecture do? This post will provide an introduction to parallel computing by exploring:

Parallel vs Serial Computing
The Types of Parallelism
The Advantages
The Applications

Parallel vs Serial Computing

Necessity is the mother of invention. This is how parallel computing was born. We have a need for speed and efficiency. These are demands that serial computing cannot meet.

Parallel Computing

  • Breaks a problem up into a series of discrete instructions
  • The instructions are executed concurrently to solve the problem
  • Uses multiple computer resources

Serial Computing

  • Breaks a problem up into a series of discrete instructions
  • These instructions are executed one at a time
  • Uses a single processor

The main difference between these types of computing is shown in the last two bullet points above. With more complex computational problems, the use of serial computing is inefficient. Parallel computing simultaneously makes use of multiple processing elements and, in doing so, increases the speed at which a problem is solved. There is no arguing with that level of efficiency. Especially, when pitted against its serial counterpart.

The Types of Parallelism

With this understanding of parallel computing in mind, it is important to note that there are different types of parallelism to be aware of. Knowledge of these main forms of parallel computing can help you with programming, from how to write to how to run a program.

Instruction-level
Task
Data
Bit-level

This type of parallelism is described as multiple, independent instructions in a program that can be executed simultaneously by a processor. Hence, allowing for an overlap in execution. Since these instructions can be evaluated in parallel, they can be reordered. This reduces execution time and doesn’t impact the correctness of the program.

This type of parallelism involves breaking down a task into sub-tasks and distributing those sub-tasks for execution. Processors are, therefore, allocated a different sub-task and they execute them simultaneously. The processors work on the same or different data. Task parallelism has the advantage of multiple operations being executed at the same time.

This type of parallelism is often contrasted with task parallelism. Data parallelism divides the data and runs the same task across different, multicore processors. This type of parallelism involves each processor performing the same operation on different pieces of data.

This type of parallelism is rooted in increasing the processor’s size. As we know, serial computing uses single-bit computers. With an increased bit-level, the processor has more power and the number of instructions executed, to complete a single task, is reduced.

The Advantages

It is evident that parallel computing allows for a more efficient path to solving computational problems but what does this mean practically? Here are some pragmatic advantages that come with parallel computing:

Parallel Computing

It is evident that there are a number of advantages to employing parallel computing to tackle computational problems. To summarise, parallel computing is the gateway to getting more accurate results in an efficient manner.

The Applications

We know that parallel computing breaks down large computational problems into instructions that can be executed simultaneously. This is ideal for processing large amounts of data. Therefore, parallelism can be applied in the fields of science, engineering, data mining, and virtual reality, to just name a few. In fact, parallel computing is at the centre of many scientific studies. Here are some examples:

In health sciences, medical imaging is one of the most vital applications of parallel computing. This has resulted in an improved image resolution and definition of MRIs, CTs and X-rays. Hence, the improvement of medical diagnoses.

In astrophysics, scientists rely on parallelism to study processes. Parallel computing allows for the simulation of processes such as the merging of galaxies and the collision of stars. This allows for better understanding of celestial objects and phenomena.

In agriculture, supply and demand forecasts are one of the biggest applications of parallel computing. The ability to include more data for processing allows for improved forecasting. This impacts legislation and the financial wellbeing of farmers.

In optimisation, parallel processing (the implementation of parallel computing in software) is the key to solving problems. Octeract Engine is built for supercomputing and employs thousands of Central Processing Units (CPUs) to speed up problem-solving and find the global optimal solution. This optimisation technology is capitalising on parallel computing. Try it out for yourself.

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