Parallel computing is a computing architecture in which multiple processors work simultaneously to carry out a task. This type of computation allows a computer processor to process multiple tasks at any given time.
Whenever we use personal computers, we’re exposed to parallel computing, as modern computers perform multiple tasks simultaneously. For instance, these electronic devices can run a Microsoft application, download a file, and play videos on YouTube concurrently. These days, innovative companies such as this have refined the concept of parallel computing by providing an open-source environment that offers more flexibility to working professionals.
The basic concept of parallel computing
For many years, computers have continued to rely on serial computing. A serial processor would complete a task one-by-one in an assigned order. If you were to ask the serial processor to calculate a sum, it would first break the problem into concrete instructions. After analyzing the instructions, the machine would execute commands one after the other.
Initially, serial processors could only process one instruction at any moment. Unfortunately, serial computing monopolized limited time resources when completing various tasks, given that the computer had to complete one task before moving on to the next function.
Despite more powerful processors, serial computing didn’t allow the powerful processor to utilize its resources to the fullest extent. Even with the additional power, it could only accomplish one task at a time.
In the 60s and 70s, scientists started using shared-memory processors that worked side-by-side on the shared data. Just ten years later, parallel computing went mainstream with the introduction of 64 Intel processors. Soon after, off-the-shelf microprocessors were available for personal computers that used massively parallel processors. As a result, the general public began to enjoy the advantages of parallel computing. Outlined below are some of these notable advantages.
Advantages of parallel computing
Parallel computing saves users a sizable amount of time by deploying multiple processors to complete a specific task. Multiple processors can also work on different tasks simultaneously. The time saved translates to fewer costs and more efficiency.
Using parallel computing, scientists can solve large problems that are often time-sensitive. When local resources are finite, parallel computing can use non-local resources to complete a job. As an additional benefit, parallel computing uses hardware resources more efficiently than serial computing.
Types of parallel computing
Bit-level parallelism is a type of parallel computing known to boost efficiency by increasing the processor word size. Increasing the processor word size enables the computer to reduce the number of steps it takes to resolve a particular problem.
Instruction-level parallelism is a process that allows particular instructions in a larger project to execute simultaneously. By contrast, most processors can only execute less than one instruction each clock cycle phase. As part of instruction-level parallelism, these regroup instructions allow them to run concurrently without affecting the overall result.
This type of parallel computing breaks down a computing task into subgroups before allocating a specific subgroup to a particular processor. The processors perform the execution of the subtasks simultaneously.
Applications of parallel computing
We need parallel computing resources at our disposal to keep pace with our high-tech global environment and its conveyor belt of software innovations. Parallel processors help us deal with enormous amounts of data, which needs deciphering for better analysis. Similarly, real-time data requires multiple processors to run simultaneously if these analysts are to interpret real-time numbers.
The advent of virtual reality, artificial intelligence, the Internet, cloud computing, blockchain, and advanced graphics are parallel computing’s brainchildren. You’d be hard-pressed to find a serial processor that can handle such complicated tasks regardless of its power.
Perhaps, the most apparent advantage of parallel computing over serial computing is the effective utilization of resources. In the traditional computing environment, only a part of the computer processor was operational at any moment while a lot of other parts remained idle. In contrast, parallel computing allows users to utilize the computer to its full potential.
Users use the terms parallel computing and parallel processing increasingly interchangeably. To make things simpler, think of parallel processing as a process that defines the number of cores and CPU running simultaneously inside a computer. The parallel running of CPU and computer cores ensures that users can perform multiple tasks at any given time. In contrast, parallel computing may refer to how the software controls these parallel processes to ensure effective collaboration.
To achieve energy efficiency in your company, consider harnessing the power of parallel computing. As our workloads continue to increase and the demand for convenience rises, prepare for parallel computing innovations in our foreseeable future that will revolutionize the industry.