Distributed Systems and Parallel Computing

                                                                                                      

Distributed Systems and Parallel Computing

     Introduction:

We all know how important computing is for us nowadays, ranging from lifesaving machines to social media platforms like Facebook. All of these use computing. Computing is divided into two types i.e. Distributed and Parallel. Both systems came into the picture in the early ’70s. Both have their flaws and both excel in their particular domain. In this blog, we will explore the fascinating concepts of distributed systems and parallel computing, and understand their benefits, challenges, and real-world applications.

 What does the term ‘Computing’ actually mean?

 Computing is a process to give you the output for the instructions that you had fed to the computer. Computing will check the availability of package and hardware systems for a range of functions. Together with functions like structuring, processing, and managing data, it will help us in scientific research and develop intelligent systems for entertainment and communication purposes. 

 Distributed Computing: 

                                                    

What are Distributed Systems?

 A distributed system is a group of independent computers or servers that work together as a single system, sharing resources, data, and computations across multiple nodes. Each node in a distributed system communicates and collaborates with other nodes to achieve a common goal. The nodes can be located in the same place (e.g., data center) or spread across different locations. Distributed systems are designed to handle a large amount of data and tasks efficiently by working in parallel, achieving better performance compared to traditional systems. Let us take a simple example to understand this better. Consider Google, it’s one of the most-used search engines and it receives a lot of requests per second and a single computer or system won’t be able to handle all at once, that’s where distributed systems come into play. A large system of virtually connected computers receives your requests and gives back your results. Now, imagine one of these computers failing to do its job, however, to your surprise, you’ll find that you still receive your results because the failed computer sends a message to other computers through the network and they fetch the results for you. 

This is one of the best examples where distributed systems are applicable. Distributed systems are used when a task is too big for a single computer or device to handle. When the workload is erratic, as it is during Cyber Monday e-commerce traffic, they are extremely helpful. These days, nearly all internet-connected online applications are constructed on top of a distributed system. Typical examples are peer-to-peer file-sharing networks like BitTorrent and cryptocurrency processing networks like Bitcoin. 

 Benefits of Distributed Systems :

 1) Scalability: Distributed systems can handle a large amount of data and tasks by adding more nodes to the system as needed, making them highly scalable. 

 2) Fault Tolerance: The fault tolerance feature is an inherent design principle of distributed systems that enables them to withstand and recover from failures. If a node fails, the workload can be automatically shifted to other available nodes, ensuring high availability and reliability.

 3) High Performance: Parallel processing in distributed systems allows for faster execution of tasks by dividing them among multiple nodes, leading to improved performance. 

 4) Flexibility: Distributed systems allow for flexibility in adding or removing nodes based on the changing requirements of the system.

 5) Cost-Effectiveness: Distributed systems can be cost-effective, as they can utilize commodity hardware and distribute the workload across multiple nodes, reducing the need for expensive hardware. 

 Real-world Applications 

 1) Companies such as Amazon, Google, and Microsoft offer cloud computing services through their respective platforms, namely Amazon Web Services, Google Cloud Platform, and Microsoft Azure. 

 2) Distributed databases like Cassandra, MongoDB, and Apache HBase. 

 3) Hadoop Distributed File System (HDFS) exemplifies a type of file system that distributes data across multiple computing nodes in a cluster. 

 Parallel Computing: 

                                         

 What is Parallel Computing? 

 A sort of computer design known as parallel computing allows a number of processors to run or process a calculation or programmer simultaneously. Massive computations can be executed more rapidly by employing parallel computing, which distributes the burden among multiple processors that each do the operation simultaneously. In computational parallelism, numerous streams of operation operate concurrently and share a shared memory to enable synchronization. Parallel processing is commonly used in operational contexts or scenarios that need a large amount of computing or processing capacity. Parallel computing’s main goal is to enhance the amount of computing power available for quicker application processing or task resolution. Parallel computing infrastructure is often located in a single facility, with many processors deployed in a server rack or different servers networked together. The application server transmits a calculation or processing request that is broken down into small pieces or components and processed in parallel on each processor or server. Bit-level, instructional-level, data, and task parallelism are all types of parallel processing. Let us understand this with the help of an example. 

Let us imagine you have sent a lot of data to your processor and you’re expecting output but as you are using a single processor it takes more time and heats the system as well. Now let us try adding another processor to your already existing system, now you have two parallelly running processors doing the same task but this time you’ll get the result in half the amount of time. This is what parallel computing is, on a smaller scale. 

 Benefits of Parallel Computing 

 1) Faster Processing: Parallel computing allows for concurrent execution of tasks, leading to faster results compared to sequential processing.

 2) Improved Scalability: Parallel computing enables the use of multiple processing units, making it easier to scale up the computation as needed.

 3) Better Resource Utilization: Parallel computing efficiently utilizes the available resources, such as CPU cores or GPUs, leading to improved resource utilization and cost savings. 

 4) Enhanced Problem Solving: Parallel computing can handle complex computations and large datasets, enabling faster and more accurate solutions to complex problems.

 5) Future-proofing: As technology advances, parallel computing becomes increasingly relevant, as it allows for better utilization of modern hardware, such as multi-core processors and GPUs. Real-world 

Applications

 1)High-performance computing clusters used for scientific research, such as NASA’s Pleiades cluster.

 2) Graphics processing units (GPUs) used for machine learning and deep learning. 

 3) Distributed computing frameworks like Apache Spark and Apache Flink. 

 Difference between Distributed Systems and Parallel Computing: 

Conclusion:

Distributed systems are expected to become more and more important in global computing as corporate developers depend more and more on them to manage applications, expedite development, deploy systems and infrastructure, and ease operations. Parallel computing, at its most basic level, is a component of the multi core processors that make our phones and laptops function effectively. It’s the concept that a computer can break down a task into components and work on them all at once. Parallel computing will keep up with the growth of data in our environment to help us make sense of it.