Difference Between Distributed System and Parallel System

In today's world, computers have become an integral part of our lives, from smartphones to desktop computers to massive data centers, all of which are interconnected. The use of multiple computers working together to solve a problem is known as parallel computing, while the use of multiple computers working together to provide a single cohesive system is known as distributed computing. In this article, we will discuss the differences between distributed and parallel systems.

What is a Distributed System?

A distributed system is a collection of independent computers that appear to the user as a single coherent system. A distributed system's primary goal is to provide users with access to shared resources and services, such as files, printers, and databases, as if they were all part of a single system.

A distributed system's architecture can be classified into two types: client-server architecture and peer-to-peer architecture. In a client-server architecture, one or more servers provide services to one or more clients. The client requests a service from the server, and the server provides it. In a peer-to-peer architecture, all computers are equal, and each computer can provide and consume services.

Distributed systems are highly scalable, fault-tolerant, and geographically dispersed, making them ideal for use in large organizations and multinational companies. A distributed system's main advantage is that it can handle a large number of requests simultaneously, making it suitable for applications that require high throughput.

What is a Parallel System?

A parallel system is a collection of interconnected processors that work together to solve a problem. The processors may be located on a single computer or multiple computers, and they communicate with each other to achieve a common goal.

A parallel system can be classified into two types: shared memory architecture and distributed memory architecture. In shared memory architecture, all processors share a single memory, and they communicate with each other using shared variables. In distributed memory architecture, each processor has its own memory, and they communicate with each other using messages.

Parallel systems are designed to solve computationally intensive problems quickly by dividing the problem into smaller sub-problems and solving them simultaneously. Parallel systems are ideal for scientific applications, such as weather forecasting, molecular modeling, and computational fluid dynamics, that require a lot of processing power.

Differences Between Distributed Systems and Parallel Systems:

Now that we have defined distributed and parallel systems, let's look at the differences between the two:

1. Architecture:

The architecture of a distributed system is based on the client-server or peer-to-peer model. On the other hand, the architecture of a parallel system is based on the shared memory or distributed memory model.

2. Communication:

In a distributed system, communication between computers is usually done using message passing, while in a parallel system, communication is usually done using shared memory.

3. Data Sharing:

In a distributed system, data sharing is done through remote procedure calls or by accessing a shared database, while in a parallel system, data sharing is done through shared memory.

4. Processing:

In a distributed system, processing is distributed among multiple computers, and each computer handles a specific task, while in a parallel system, processing is done simultaneously by multiple processors, each handling a specific task.

5. Scalability:

Distributed systems are highly scalable, as more computers can be added to the system to handle more requests. In contrast, parallel systems have a limited scalability, as the number of processors is limited by the hardware.

6. Fault Tolerance:

Distributed systems are designed to be fault-tolerant, as they can continue to function even if one or more computers fail. Parallel systems are not fault-tolerant, as a failure of one processor can cause the entire system to fail.

7. Use Cases:

Distributed systems are commonly used in large organizations and multinational companies, while parallel systems are used in scientific and engineering applications that require a lot of processing power, such as weather forecasting, molecular modeling, and computational fluid dynamics.

8. Programming Model:

In a distributed system, the programming model is typically based on remote procedure calls or message passing, while in a parallel system, the programming model is typically based on threads or processes.

9. Data Movement:

In a distributed system, data movement between computers can be slow due to network latency and bandwidth limitations. In contrast, data movement in a parallel system is fast due to shared memory and high-speed interconnects.

10. Cost:

Distributed systems are typically more expensive than parallel systems because they require additional hardware and software to manage the system's complexity. In contrast, parallel systems can be built using commodity hardware and open-source software.

Conclusion:

In summary, distributed and parallel systems are two different types of computer systems that have different architectures, communication models, and programming models. Distributed systems are designed to provide a single cohesive system by combining multiple independent computers, while parallel systems are designed to solve computationally intensive problems by dividing the problem into smaller sub-problems and solving them simultaneously.

Distributed systems are highly scalable and fault-tolerant, making them ideal for large organizations and multinational companies. Parallel systems, on the other hand, are ideal for scientific and engineering applications that require a lot of processing power.

In conclusion, the choice between distributed and parallel systems depends on the application's requirements, the available hardware and software, and the desired performance and scalability.