Distribute vs Parallel Computing

Distributed Computing

Distributed computing may be a field that studies the distributed systems criteria but in real life the distributed systems are systems that have multiple computers located in different locations. These computers during a distributed system work on an equivalent program. The program is split into different tasks and allocated to different computers. The computers communicate with the assistance of message passing. Upon completion of computing, the results collated and presented to the user.

Parallel Computing

Parallel computing  is a model that divides a task into multiple sub-tasks and executes them simultaneously and this helps to extend its speed and efficiency too. Here, a drag is weakened into multiple parts. Each part is then broken down into a variety of instructions. These parts are allocated to different processors which execute them at the same time. This increases the speed of execution of programs as an entire 

Where are they used?-

Parallel computing is often used in places where they are required for a higher as well as faster processing power. For example you can take supercomputers. Since there are not any lags within the passing of messages, these systems have high speed and  efficiency too. Distributed computing is used when computers are located at two different locations. In these scenarios, speed is usually not an important factor but they preferred a choice of scalability (it required).

Distributed vs Parallel Computing-

1.  Number of Computer Systems Involved in Parallel Computing, generally  there is one computer with multiple processors in it. Multiple processors within an equivalent computing system that help to execute instructions simultaneously without any problem and all the processors work towards completing an equivalent task. Thus they share all resources and data. In distributed computing, there are several computer systems involved and these multiple autonomous computer systems work on the divided tasks. These computer systems are generally located at  different locations.

2.   Now let’s talk about  dependency Between Processes, In parallel computing, the tasks to be solved by dividing it into multiple smaller tasks and then taking these smaller tasks are assigned to multiple processors and the result of 1 task could be the input of another task. This helps increase dependency between the processors. We can also say, parallel computing environments are codependent on each other(or tightly coupled). Some distributed systems are loosely coupled, while others are tightly coupled.

3.   You must be thinking which is more Scalable? If we talk about parallel computing, In parallel computing environments, the number of processors you can add is limited. This is because of the bus which is connecting to the processors and if it’s not restricted it can’t make memory to handle it so therefore the memory can handle a limited number of connections. There are limitations on the amount of bus processors that can connect them so there is limitation to it, So to be more clear all these limitations make Parallel Computation less scalable than Distributed computation. To add more to this topic distributed computing environments are more scalable. This is because in distributed computing the computers are connected with the networks and they usually communicate to each other by passing messages.

4.   Resource Sharing in systems implementing, if we talk about parallel computing then all the processors share the same memory they also share an equivalent communication medium and as well as same network. The processors communicate with one another with the assistance of shared memory. Distributed systems, on the opposite hand, have their own memory and processors.

5.    In parallel systems all the processes share an equivalent master clock for synchronization. Since all the processors are hosted on an equivalent physical system, They actually don’t need any particular synchronization algorithms. But on the other hand, In distributed systems, the individual processing systems don’t have access to any central clock. Hence, they need to implement a synchronization algorithm.

Conclusion-

So in conclusion, The Problems associated with these computing models are described. Such a comparison in different aspects will help us recognize the computing models since few features of these computing models are comparable. It also helps us in distinguishing the similarities and differences among these two(Parallel and distributed). After all the above, it can be concluded that if we put more efforts on parallel computing the level of parallelism can be improved. We can also improve the computing speed by this. Also efforts are needed to place for the support of fault tolerance in parallel systems. Lot of effort has been done in this specific field, yet at the same time more efforts are needed to be done.

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