Parallel Computing and Its Advantage and Disadvantage

In the world of computing, where data and processing demands are ever-increasing, parallel computing has emerged as a game-changer. This innovative approach to computation allows multiple tasks or processes to be executed simultaneously, significantly boosting performance and efficiency. In this article, we will delve into what parallel computing is, explore its advantages, and discuss its disadvantages.

What is Parallel Computing?

Parallel computing is the use of two or more processors (cores, computers) in combination to solve a single problem. It is a type of computing architecture in which several processors execute or process an application or computation simultaneously. Traditional computing follows a sequential execution model, where tasks are executed one after the other. In contrast, parallel computing breaks down complex problems into smaller, independent tasks that can be processed simultaneously. These tasks are distributed among multiple processing units, such as CPU cores or computer nodes, to achieve a substantial reduction in processing time. Most supercomputers employ parallel computing principles to operate.

This type of computing is also known as parallel processing. The primary objective of parallel computing is to increase the available computation power for faster application processing or task resolution. Parallel computing infrastructure is standing within a single facility where many processors are installed in one or separate servers which are connected together. It is generally implemented in operational environments/scenarios that require massive computation or processing power.

In April 1958, S. Gill (Ferranti) discussed parallel programming and the need for branching and waiting. Also in 1958, IBM researchers John Cocke and Daniel Slotnick discussed the use of parallelism in numerical calculations for the first time. Burroughs Corporation introduced the D825 in 1962, a four-processor computer that accessed up to 16 memory modules through a crossbar switch.

Historically parallel computing was used for scientific computing and the simulation of scientific problems, particularly in the natural and engineering sciences, such as meteorology. This led to the design of parallel hardware and software, as well as high-performance computing.

To deal with the problem of power consumption and overheating the major central processing unit (CPU or processor) manufacturers started to produce power-efficient processors with multiple cores. The core is the computing unit of the processor and in multi-core processors, each core is independent and can access the same memory concurrently. Multi-core processors have brought parallel computing to desktop computers. Thus parallelization of serial programs has become a mainstream programming task.

In 2012 quad-core processors became standard for desktop computers, while servers have 10 and 12 core processors.

Parallel computers based on interconnected networks need to have some kind of routing to enable the passing of messages between nodes that are not directly connected. The medium used for communication between the processors is likely to be hierarchical in large multiprocessor machines.

Advantages of Parallel Computing

Disadvantages of Parallel Computing

1. Complexity

Implementing parallel computing can be complex and challenging. Developing algorithms and programs that can be effectively parallelized requires careful design and consideration of dependencies between tasks. Additionally, debugging and testing parallel programs can be more challenging than sequential ones.

2. Synchronization Overhead

In parallel computing, tasks often need to communicate and synchronize with each other. This communication overhead can introduce complexities and potential bottlenecks, impacting overall performance. Efficient synchronization mechanisms and load balancing are essential to mitigate these issues.

3. Amdahl's Law

Amdahl's Law states that the overall speedup gained by parallelizing a computation is limited by the sequential portion of the algorithm. In other words, if a significant portion of the computation must be executed sequentially, the potential speedup from parallel computing is limited, and the benefits may not be as substantial as anticipated.

4. Cost and Infrastructure

Parallel computing often requires specialized hardware, such as multi-core processors, GPUs, or clusters of interconnected computers. Acquiring and maintaining such infrastructure can be costly, especially for smaller organizations or individuals.

Conclusion

Parallel computing is a powerful approach that harnesses the capabilities of multiple processing units to improve performance and handle complex tasks efficiently. Its ability to process tasks simultaneously offers significant advantages in various domains, from scientific research to multimedia applications. However, parallel computing comes with its challenges, including the need for careful design, synchronization overhead, and potential costs associated with specialized hardware. Despite its disadvantages, parallel computing continues to be a critical and indispensable technique, paving the way for faster, more efficient, and scalable computing in the modern era.