Exploring the Differences Between Parallel and Distributed Computing
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Parallel vs distributed computing is all about the choice between two powerful technologies. At first glance, they may seem to serve similar purposes.
There are important differences between the two approaches, however. Understanding these differences is key to achieving the best possible results during computations.
Let’s examine the advantages and disadvantages of parallel vs distributed computing. Then we’ll look at the key differences between them, and see how these suit them to different use cases.
What is Parallel Computing?
Parallel computing lets you perform computational tasks using multiple processors simultaneously.
Tasks are divided into sub-tasks, which are then broken down further into instructions. Each instruction is then assigned to a different processor.
Processors connect via a shared memory space. The processors then complete these tasks simultaneously.
Parallel computing is often used for massive processing power and complex calculations. Parallel computing can help to carry out processes efficiently. This can save a great deal of time.
There are three main levels of parallel computing:
and task-level parallelism
What are the Advantages of Parallel Computing?
There are several advantages to using parallel computing.
Parallel computing is able to perform computations much faster than traditional, serial computing. This is because it processes multiple instructions simultaneously using different processors. The more processors available, the faster the speed.
This makes problem-solving faster by reducing the time taken to get results. It also increases the speed of decision-making.
Parallel computing executes tasks and processes concurrently. This increases the throughput of the system. This is important in scenarios where a high volume of data needs processing in a limited amount of time.
A parallel computing system can be easily scaled by adding or removing processors. The overall computational power of a system can increase or decrease as necessary.
If more computational power is suddenly needed, the system can expand as required. Think of it as akin to a call center, where new phone lines can be added to a PBX system to handle higher volumes of calls.
Better Resource Utilization
Parallel computing systems distribute their workload across the hardware resources available to them. This limits the overutilization or underutilization of specific resources. This improves the overall efficiency of the system.
What are the Disadvantages of Parallel Computing?
While using parallel computing has many benefits, there can also be drawbacks.
Parallel computing may require specialized software and hardware to fully realize its potential. This can result in larger initial costs when first setting up the system.
Costs can increase if additional processors are needed to scale the system.
The algorithms needed for parallel computing are more complex than serial computing.
This makes it harder to manage data distribution and communication between parallel processors. It’s also more difficult to debug parallel computing solutions than their serial alternatives.
Parallel computing often requires synchronization and communication mechanisms between processors to ensure consistency. Using these mechanisms can raise overheads, and create issues with network latency. This can work to reduce the performance gains in some systems.
What is Distributed Computing?
Distributed computing connects computers so that they can act as one powerful machine.
They work together to perform complex computations. This allows them to perform tasks that no single computer in the network would be able to achieve.
Computers are either connected through a local network if they are geographically close to one another, or through a wide area network (WAN) if they are geographically distant.
A distributed system is made up of a variety of different devices, such as computers, mainframes, and minicomputers. Each device within the system is referred to as a ‘node’, with a group of nodes known as a ‘cluster.’
Nodes in the system communicate with one another by passing messages through the network. It’s even possible to remotely access and control other nodes within a cluster using a remote desktop management tool.
During distributed computing, the various steps in a process will be distributed to the most efficient machine in the network.
For example, user interface processing will occur on the computer being accessed by the user, while application processing will occur on a remote machine.
Database accessing and processing algorithms will take place on another remote machine. This is usually one that can provide centralized access for a range of processes.
What are the Advantages of Distributed Computing?
Distributed computing has several advantages.
Flexibility & Adaptability
Distributed systems can change to meet changing requirements. It’s possible to add or remove new nodes as necessary. Developers can change or reconfigure the system entirely as workloads change.
Distributed computing systems are well-suited to organizations that have varying workloads.
Using distributed computing, organizations can create systems that span multiple geographic locations. This is perfect for multinational corporation systems. These often require collaboration between users in different locations, using any number of domains from Only Domains.
Distributed computing facilitates global collaboration. Users in different geographic locations can access and contribute to shared resources.
Data Redundancy & Backup
A distributed system will often replicate data across multiple nodes. This helps to provide data redundancy and backup capabilities. In the event of a disruption such as hardware failure, data availability will still be maintained thanks to the other nodes in the system.
Like parallel computing, distributed computing enables the parallel execution of tasks. A distributed system provides improved performance and faster execution times. It does this by carrying out tasks across multiple nodes.
What are the Disadvantages of Distributed Computing?
Distributed computing has many benefits. But there are also pitfalls that must be avoided.
Robust security measures must be put in place to ensure secure communication and that data is protected across the entire system.
Software Complexity & Compatibility
Writing code that will function correctly in a distributed computing environment can be tricky, as it must be able to handle issues such as data partitioning, synchronization, and task distribution.
It can also be challenging to ensure that the software running on different nodes is all compatible with one another. Compatibility issues can arise from differences in operating systems, libraries, and hardware configurations.
Resource allocation and load balancing can present a challenge for distributed computing systems.
Nodes may feature varying levels of computing power, storage, and memory. This can lead to issues with performance, such as increased latency. This impacts the responsiveness of distributed systems. This is especially the case with tasks that require frequent internode communication.
The Differences Between Parallel and Distributed Computing
Now we understand the pros and cons of both parallel and distributed computing. From here, it’s time to take a closer look at the direct differences in parallel vs. distributed computing.
Number of Computers
Parallel computing usually involves one computer with multiple processors. Distributed computing uses multiple distinct computers.
All processors involved in parallel computing share the same memory and use it to communicate with each other.
In distributed computing, each computer has its own memory.
Both parallel and distributed computing systems are scalable. This is usually more easily achieved in distributed systems, where new computers or other devices can be easily added to the network.
Scaling a parallel system can be more difficult. The memory within a single computer can only handle so many processors working at the same time.
Distributed computing systems utilize synchronization algorithms. In parallel computing systems, all processors share a single master clock.
In parallel computing, the processors communicate with one another using a bus.
In distributed computing, computers communicate with one another via the network.
When to Use Parallel Computing
Parallel computing is commonly used for complex computational problems, which can be divided into smaller tasks.
Executing these smaller tasks simultaneously leads to:
Faster execution times
Better resource utilization
Scenarios which often involve the use of parallel computing, include:
Large-scale data processing, e.g. big data analytics
Scientific simulations and modeling, e.g. climate modeling, bioinformatics
Image processing, e.g. medical imaging
Video processing, e.g. video color correction
Machine learning, e.g. financial risk management algorithms
Video game development, e.g. computer physics simulations
When to Use Distributed Computing
Distributed computing is often used to build and deploy powerful applications. It can also be used when a computational task can be divided into smaller components.
It’s especially useful in scenarios that demand:
Frequent resource sharing
High fault tolerance
Scenarios that often involve the use of distributed computing include:
Parallel and Distributed Computing: Similar, Yet Different
Both parallel and distributed computing spread out the workload of computational tasks. This leads to increased efficiency and faster results.
Yet, the two technologies feature some key differences. This makes parallel and distributed computing suited to different tasks.
In the question of parallel vs. distributed computing, you can only choose the right technology for your use case if you understand the differences between the two approaches. Choosing the right technology for your application will help you achieve the best possible results.
Disclaimer: The author is completely responsible for the content of this article. The opinions expressed are their own and do not represent IEEE’s position nor that of the Computer Society nor its Leadership.