Parallel vs Distributed Computing
What is Parallel Computing?
Parallel computing is a model that divides a task into multiple sub-tasks and executes them simultaneously to increase the speed and efficiency.
Here, a problem is broken down into multiple parts. Each part is then broke down into a number of instructions.
These parts are allocated to different processors which execute them simultaneously. This increases the speed of execution of programs as a whole.
Parallel computing is divided into three sorts, or “levels”: bit, instruction, and task.
Bit-level parallelism: Uses larger “words” to reduce the number of instructions the processor needs to complete an operation. A “word” is a fixed-sized piece of data processed as a unit by the instruction set or the hardware of the processor.
Instruction-level parallelism: Allows processors to execute more than one instruction per clock cycle by using a stream of instructions (the oscillation between high and low states within a digital circuit).
Task-level parallelism: Distributes computer code across numerous processors in order to perform multiple tasks on the same data at once.
What is Distributed Computing?
Even though the premise is the same, distributed computing differs from parallel computing.
The field of distributed computing is concerned with the study of distributed systems. Distributed systems are those that have several computers scattered around the globe.
In a distributed system, all of the computers are working on the same programme. The application is broken down into tasks and assigned to several computers.
Message forwarding is used to communicate between computers. The result of the computation is compiled and given to the user.
Distributed computers have two major benefits:
Simple scalability: To expand the system, simply add more computers.
Redundancy: Because the same service is provided by multiple machines, it can continue to operate even if one (or more) of them fails.
Key Differences Between Parallel Computing and Distributed Computing :
While parallel and distributed computers are both significant technology, they vary in numerous key ways.
Difference #1: Number of Computers Required
In most cases, parallel computing involves a single machine with several processors. Distributed computing, on the other hand, entails the collaboration of numerous autonomous (and frequently geographically separated and/or remote) computer systems to complete a set of tasks.
Difference #2: Scalability
Because a single computer’s memory can only support so many processors at once, parallel computing systems are less scalable than distributed computing systems. Additional computers can always be added to a distributed computing system.
Difference #3: Memory
All processors in parallel computing share the same memory, and the processors communicate with each other using this shared memory. On the other hand, distributed computing systems have their own memory and CPUs.
Difference #4: Synchronization
For synchronisation, all processors in parallel computing employ a single master clock, whereas distributed computing systems use synchronisation techniques.
Difference #5: Application
Distributed computing is used to share resources and improve scalability, whereas parallel computing is used to increase computer performance and for scientific computing.
When to Use Parallel Computing: Examples
This computing technique is appropriate for sophisticated simulations or modelling. Seismic surveying, computational astrophysics, climate modelling, financial risk management, agricultural estimations, video colour correction, medical imaging, drug development, and computational fluid dynamics are all examples of common applications.
When to Use Distributed Computing: Examples
Distributed computing is ideal for creating and delivering robust applications that span multiple users and locations. Distributed computing is already in use by anyone doing a Google search. Many aspects of “contemporary business,” such as cloud computing, edge computing, and software as a service, have been shaped by distributed system architectures (SaaS).
Which Is Better: Parallel or Distributed Computing?
It’s difficult to state whether parallel or distributed computing is “better” because it depends on the use case (see section above). If you need pure processing capacity and operate in a scientific or other highly analytical field, parallel computing is certainly the way to go. If you need scalability and robustness and can afford to operate and maintain a computer network, distributed computing is undoubtedly the way to go.
Which is More Scalable?
The amount of processors you can add in parallel computing environments is limited. Because the bus between the CPUs and the memory can only support a certain number of connections, this is the case.
The number of processors that the bus connecting them and the memory can handle is limited. Because of this constraint, parallel systems are less scalable.
Scalability is improved in distributed computing settings. This is due to the fact that the computers are linked by a network and communicate by sending messages.
Blog by:
Swapnil Mhoprekar, Gautam Mudawadkar, Prathmesh Nagpure, Shrutika Nandurkar, Niharika Hande, Vedant Parvekar
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