It’s not wrong to say the idea of “parallel computing” and “distributed computing” is a bit confusing, even for those who are familiar with tech jargon. Both parallel computing and distributed computing divide complex computational problems among multiple processing units. However, they differ in their architectures and communication methods. This blog post aims to clarify the key differences between the two concepts. And we hope this guide helps you with which method you need to use depending on your requirement.
Parallel Computing:
Parallel computing is when a lot of computers work together to solve a complex problem. They break the problem into small pieces that each processor can do. All the processore work at the same time and then combine their answers to get one final answer.
This method of computing involves multiple processing units that share a common memory space to communicate with one another. It’s similar to horizontal scaling, using multiple processors to accelerate computation on a single problem.
Pros of parallel computing:
- Faster computation: Parallel computing can reduce computation time by dividing a problem into smaller subproblems and running them simultaneously on multiple processing units.
- Scalability: Adding more processing units to parallel computing can increase its scalability and efficiency, making it a suitable solution for managing extensive computational workloads with flexibility.
- Shared memory: Parallel computing simplifies programming and reduces communication overhead by enabling processing units to easily share memory.
Cons of parallel computing:
- Complexity: Parallel computing can be more complex than sequential computing, and can require specialized programming skills to effectively manage concurrency and synchronization between processing units.
- Limited scalability: Parallel computing on shared memory architectures may have limitations on the number of processing units due to practical reasons..
- Communication overhead: Although parallel computing can decrease computation time, it may lead to higher communication overhead among processing units particularly for tasks that require frequent data exchanges.
Distributed Computing:
In contrast to parallel computing, distributed computing uses multiple computers to solve one problem. Each computer works on a different part of the problem and the results are shared and combined to arrive at the final solution.
On the other hand, distributed computing refers to breaking down a problem into smaller subproblems and assigning those subproblems to various independent computing nodes. (such as several computers connected over a network).
In distributed computing, every processing unit generally has its own local memory space and communicates with other processing units through message passing. Distributed computing is similar to “vertical scaling” as it utilizes multiple processors to tackle different aspects of a problem at the same time. This approach is beneficial when dealing with memory-intensive tasks because each computer provides its own memory resources.
Pros of distributed computing:
- Scalability: Scaling up distributed computing is easy by adding more computing nodes, making it the ideal solution for efficiently handling large computational workloads.
- Fault tolerance: Distributed computing can enhance resilience against hardware failures or network disruptions. Because it is possible to use of other nodes for computation.
- Geographic distribution: Distributed computing allows for computations to be performed on multiple nodes located in different physical locations. This can be useful for processing large datasets that are geographically dispersed.
Cons of distributed computing:
- Communication overhead: Communication between computing nodes can cause delays in computation and lead to increased network traffic in distributed computing.
- Complex programming: Managing communication and coordination between nodes in distributed computing often requires specialized programming skills and techniques.
- Security: Distributed computing may lead to additional security risks, such as data breaches or unauthorized access to computing nodes.
Use Cases:
Parallel computing is a good method for tackling problems that involve a lot of computations. It is particularly useful in scientific computing, simulations, and analyzing large amounts of data. Using this method it is more convenient to perform the calculations spread across multiple processors.
In contrast, distributed computing is ideal for tasks involving massive volumes of data or necessitating cooperation and communication among several processing nodes. Thus, it is well-suited for data-focused activities like machine learning and big data processing. As well as web and cloud-based applications using multiple servers which handle high volumes of requests can use distributed computing.
It is important to understand that the information provided is only a set of recommended practices, and the best solution between parallel computing and distributed computing will depend on the unique needs and limitations of the particular issue at hand. Sometimes, it may be advantageous to use a combination of both approaches in a hybrid fashion.
