Introduction to Parallel Computing
This article will provide you a basic introduction and later will explain in detail about parallel computing. Before moving on to the main topic first let us understand what is parallel Computing.
What is Parallel Computing?
The simultaneous execution of many tasks or processes by utilizing various computing resources, such as multiple processors or computer nodes, to solve a computational problem is referred to as parallel computing. It is a technique for enhancing computation performance and efficiency by splitting a difficult operation into smaller sub-tasks that may be completed concurrently.
Tasks are broken down into smaller components in parallel computing, with each component running simultaneously on a different computer resource. These resources may consist of separate processing cores in a single computer, a network of computers, or specialized high-performance computing platforms.
Various Methods to Enable Parallel Computing
Different frameworks and programming models have been created to support parallel computing. The design and implementation of parallel algorithms are made easier by these models' abstractions and tools. Programming models that are often utilized include:
Types of Parallel Computing
There are 4 types of parallel computing and each type of parallel computing is explained below
1. Bit-level parallelism: The simultaneous execution of operations on multiple bits or binary digits of a data element is referred to as bit-level parallelism in parallel computing. It is a type of parallelism that uses hardware architectures' parallel processing abilities to operate on multiple bits concurrently.
Bit-level parallelism is very effective for operations on binary data such as addition, subtraction, multiplication, and logical operations. The execution time may be considerably decreased by executing these actions on several bits at the same time, resulting in enhanced performance.
For example, consider the addition of two binary numbers: 1101 and 1010. As part of sequential processing, the addition would be carried out bit by bit, beginning with the least significant bit (LSB) and moving any carry bits to the following bit. The addition can be carried out concurrently for each pair of related bits when bit-level parallelism is used, taking advantage of the capabilities of parallel processing. Faster execution is possible as a result, and performance is enhanced overall.
Specialized hardware elements that can operate on several bits at once, such as parallel adders, multipliers, or logic gates, are frequently used to implement bit-level parallelism. Modern processors may also have SIMD (Single Instruction, Multiple Data) instructions or vector processing units, which allow operations on multiple data components, including multiple bits, to be executed in parallel.
2. Instruction-level parallelism: ILP, or instruction-level parallelism, is a parallel computing concept that focuses on running several instructions concurrently on a single processor. Instead of relying on numerous processors or computing resources, it seeks to utilize the natural parallelism present in a program at the instruction level.
Instructions are carried out consecutively by traditional processors, one after the other. Nevertheless, many programs contain independent instructions that can be carried out concurrently without interfering with one another's output. To increase performance, instruction-level parallelism seeks to recognize and take advantage of these separate instructions.
Instruction-level parallelism can be achieved via a variety of methods:
3. Task Parallelism
The idea of task parallelism in parallel computing refers to the division of a program or computation into many tasks that can be carried out concurrently. Each task is autonomous and can run on a different processing unit, such as several cores in a multicore CPU or nodes in a distributed computing system.
The division of the work into separate tasks rather than the division of the data is the main focus of task parallelism. When conducted concurrently, the jobs can make use of the parallel processing capabilities available and often operate on various subsets of the input data. This strategy is especially helpful when the tasks are autonomous or just loosely dependent on one another.
Task parallelism's primary objective is to maximize the use of available computational resources and enhance the program's or computation's overall performance. In comparison to sequential execution, the execution time can be greatly decreased by running numerous processes concurrently.
Task parallelism can be carried out in various ways few of which are explained below
4. Superword-level parallelism
Superword-level parallelism is a parallel computing concept that concentrates on utilising parallelism at the word or vector level to enhance computation performance. Architectures that enable SIMD (Single Instruction, Multiple Data) or vector operations are particularly suited for their use.
Finding and classifying data activities into vector or array operations is the core concept of superword-level parallelism. The parallelism built within the data may be fully utilized by conducting computations on several data pieces in a single instruction.
Superword-level parallelism is particularly beneficial for applications with predictable data access patterns and easily parallelizable calculations. In applications where a lot of data may be handled concurrently, such as scientific simulations, picture and video processing, signal processing, and data analytics, it is frequently employed.
Applications of Parallel Computing
Parallel computing is widely applied in various fields and a few of its applications are mentioned below
Advantages of Parallel Computing
Disadvantages of Parallel Computing