Online Parallel and Distributed Computing Assignment Help
Parallel and distributed computing is an active area of research. The techniques that have been involved have significant applications in areas such as management, engineering, social sciences, and natural sciences. If you are having a tough time with computer science assignments then you have the option to avail Parallel and Distributed Computing assignment help from BookMyEssay. We will supply you the assignment within the deadline, which will help you to go through them. Our online experts are always available to provide you with the most authentic and reliable academic writing services because they have immense knowledge about the subject-matter.
What is Parallel and Distributed Computing?
When the computer systems started, instructions were executed to the computer serially on a single-processor system, executing a single instruction at one time before proceeding to the next one. With the improvement in technology over the years, multiple instructions were executed in parallel to the multi-processor systems. This process is known as Parallel computing.
In distributed computing, computers worked and communicated with one another with complicated tasks on a network. Distributed computing targets to share resources and enhance the scalability of a computer system.
Parallel computing enhance performance for scientific research. The memory, number of processors, and system resources that are allocated from one location to parallel computing are limited.
Some Examples of Parallel and Distributed Computing
Some of the examples are mentioned in our Parallel and Distributed Computing case study assignment help as follows:
- Internet
- Email systems
- Storage systems/Cloud file backup
- Peer-to-peer network and Intranet
- Grid/Cloud computing
- Distributed supercomputers
- Electronic banking
- Travel reservation system
Parallel Versus Distributed Computing
Some of the differences between Parallel and Distributed Computing have been highlighted by our best Australian writers in Parallel and Distributed Computing assignment help as follows:
- Parallel computing is a kind of computation where multiple processors execute several tasks simultaneously. In distributed computing, the networked computers coordinate and communicate the work via message to reach a common goal.
- In parallel computing, you need a single computer. In distributed computing, multiple computers are involved.
- In parallel computing, several processors do processing whereas in distributed computing, computers depend on message passing.
- All processors in a parallel computing share one master clock to synchronize. There is not a global clock in a distributed computing. It makes use of synchronization algorithm.
- In parallel computing, computers will have either distributed and shared memory. In distributed computing, every computer has its own memory.
- Parallel computing is mainly for scientific computing and increasing performance. Distributed computing can share resources and enhance scalability.
Uses of Parallel Computing
The uses of Parallel computing are as follows:
- Uses multiple processors
- Used for concurrency
- Used for very high performance
- Designed for large calculations
Uses of Distributed Computing
The uses of Distributed Computing are as follows:
- Comprises of a network of computers
- Used for sharing resources
- Used for availability and consistency
- Designed to meet failures
Advantages of Distributed Computing
- Modular and Scalable growth: Distributed computing is scalable because they work across various scales and machines horizontally.
- Fault tolerance: Distributed computing systems are more fault tolerant compared to single machines. Business running several machines on two data centers mean an app will work even if the data center is offline. This gives more reliability.
- Low Latency: It has low latency and gives better performance. The software runs on several modes that result in more complexity and higher cost.
- Cost Effectiveness: Distributed computing is more cost effective than a huge centralized system. The primary cost is more than standalone systems, however, after a certain point, there are more economies of scale.
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