#parallel computing

When was the last time you cheered in a theater?

For us, it was during the epic final battle scene in Avengers: Endgame!

Picture this scenario for a moment (spoilers ahead – don’t hold it against us!):

Thanos isn't alone. Imagine ten versions of him attacking Earth simultaneously! To stop them, the Avengers need to work seamlessly as a team. Iron Man might defend California, Thor could battle in London, and Black Panther might rally troops in Wakanda. Each Avenger would use their unique strengths and resources. Seems feasible, right?

Now, consider each Avenger fighting Thanos one-on-one, taking turns based on a strategy. By the time the last Avenger strikes, Thanos would surely be defeated.

In the first scenario, the Avengers work together simultaneously, tackling different threats. In the second, they fight sequentially, one after another. This analogy illustrates the difference between Distributed Computing and Parallel Computing.

So, let's delve deeper and explore these computing approaches and their distinctions. Read on! TechDogs-"The Difference Between Distributed Computing And Parallel Computing" "Computing is not about computers anymore. It is about living."

This quote by Nicholas Negroponte, founder of the Massachusetts Institute of Technology's Media Lab, highlights how deeply computing has integrated into our daily lives and work. Computing has revolutionized information processing, enhancing productivity across various industries. However, as we rely more on computing, our demands for speed and efficiency have also increased.

Two primary strategies have emerged to meet these demands: Distributed Computing and Parallel Computing. Each has its unique strengths, making it essential to choose the right approach depending on your specific needs and goals.

Join us as we explore the differences between distributed and parallel computing – but first, let’s understand both approaches.

Understanding Distributed Computing

Imagine your business hires a team of experts, each a specialist in their field. That would solve most of your problems and challenges, wouldn’t it?

Distributed Computing is similar, but for computing. Instead of a single computer handling everything, multiple smaller computers, called nodes, are connected by a network to work together.

By collaborating, these nodes can tackle complex tasks that a single computer couldn't. Each node handles the job it's best suited for. One might manage visual processing, another performs complex calculations, and the next excels at data storage. They communicate and share information to complete tasks quickly and efficiently.

For example, consider how this works for a weather forecasting service. One computer gathers data from satellites, another crunches numbers to simulate weather patterns, and a third displays the forecast on your phone. Now, that’s teamwork!

Let’s explore the advantages of distributed computing!

Advantages Of Distributed Computing

Distributed Computing offers a powerful alternative to traditional computing by combining the resources of multiple computers. Here are the key advantages it offers:

Flexibility & Adaptability

Distributed Computing is like a team that can adjust on the fly. New computers (nodes) can be added or removed as needed, allowing the system to adapt to changing workloads. This makes it ideal for organizations with fluctuating demands, as resources can be easily scaled up or down.

Global Collaboration

Distributed Computing allows users in different locations to access and contribute to shared resources. This is perfect for multinational corporations where collaboration across geographic boundaries is essential.

Data Redundancy & Backup

Distributed Computing boasts abilities like having multiple copies of important data stored in different locations. Since information can be replicated across multiple nodes, it ensures full availability even if a single node experiences hardware or software failure.

Now that we understand Distributed Computing and its advantages, let’s look closely at Parallel Computing!

Understanding Parallel Computing

If you're hosting a giant feast at home, managing everything alone would be challenging, right? From cooking the food, placing decorations, making the house comfortable, and so on! How about a helping hand?

Parallel Computing follows a similar concept but for computers. Instead of one processor handling everything, it uses multiple processors working together. A big task, such as processing a ton of data, gets broken down into smaller chunks and each processor tackles its assigned chunk. Just as you would assign someone to set up decorations and someone else to serve the food.

Like your helpers, these processors work on their tasks at the same time while sharing a common space. This teamwork lets them finish the job quickly, just like your party crew gets everything ready swiftly!

Let’s get to the advantages of Parallel Computing.

Advantages Of Parallel Computing

Here are various advantages of Parallel Computing – our top picks are:

Enhanced Speed

It accelerates computations by processing instructions simultaneously on multiple processors. This directly reduces processing time and provides faster results, making it ideal for time-sensitive tasks.

Scalability

Additional processing power can be readily added or removed based on computational demands. This flexibility allows for dynamic resource allocation, adapting the system's capacity to meet fluctuating workloads.

Better Resource Utilization

Parallel Computing distributes the workload across available hardware resources and prevents overutilization or underutilization. This ensures optimal resource allocation and enhances the system’s overall efficiency.

Faster Decision-Making

The speed advantage translates to faster turnaround times for results and lets you make faster decisions with confidence. Parallel Computing empowers users with faster processing capability, thereby significantly reducing the decision-making time.

Parallel Computing offers a compelling approach to high-performance computing and its wide range of advantages make it a valuable tool for various applications across industries.

Now that we have understood both Distributed and Parallel Computing, let’s understand the differences between them!

The Difference Between Distributed Computing And Parallel Computing

Both Parallel and Distributed Computing tackle complex tasks by dividing them into smaller chunks. However, how they achieve this teamwork differs!

Here are five key differences:

The Team Size

Parallel Computing works with a single computer that has multiple processors acting like a well-oiled team. Distributed Computing, on the other hand, utilizes a larger crowd - multiple independent computers working together on a network.

Communication

In Parallel Computing, all processors share a single memory space to communicate and access data. Distributed Computing approaches it by giving each computer its own memory and communicating with others over a network.

Synchronization

Parallel Computing systems utilize a single master clock to ensure all processors are in sync. This is similar to a team working together with a shared schedule or deadline. Distributed Computing systems, due to their reliance on network communication, require more complex synchronization algorithms to maintain consistency.

Scaling Up

Both systems can scale as needed, but Distributed Computing offers more flexibility. Adding new computers to the network is simpler than adding processors to a single machine, which can become limited by its internal memory.

Application-specific

While Parallel Computing is ideal for businesses with large, single-site workloads that benefit from fast communication and shared memory, Distributed Computing is perfect for businesses with geographically dispersed operations, massive datasets, or collaborative projects.

In essence, Parallel Computing is like a tightly knit team working within a single machine, while Distributed Computing leverages a network of independent, expert workers for large-scale tasks.

To Sum Up

The phrase "many hands make light work" perfectly captures the essence of both Parallel and Distributed Computing. By dividing complex tasks into smaller pieces, they achieve impressive results. While they share this core concept, they differ in their approach.

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Back in 2016, I tried to create parallel computing. Since using VirtualBox, the performance is not felt. And just trying out the technical system. As a result, I tried physically using Knoppix Linux which is used by several computers that are networked via LAN.

Modern Computational Finance: Parts I and II free on SSRN for a limited time

Free on SSRN for a limited time: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3735414

parts i and ii free for a limited time on SSRN

The first two parts (out of three) of my book Modern Computational Finance (Wiley, 2018) are complimentarily available on SSRN only in December 2020.

Part I teaches necessary C++ foundations with a focus on parallel computing. Part II summarizes the…