The term parallel computing might sound strange to some people out there, but it’s a highly essential term that ought to be known. That is why, in this guide, we would talk about what parallel computing is, and its various types. Do well to read with keen eyes.
What is Parallel Computing?
Parallel computing is a form of computing style in which numerous processors implement or process an application or computation simultaneously. Parallel computing helps in performing large computations by dividing the workload between more than one processor, all of which work through the computation at the same time. Most supercomputers use parallel computing principles to operate.
Parallel computing is also known as parallel processing.
Parallel processing is generally applied in operational environments/scenarios that require enormous computation or processing power. The main objective of parallel computing is to increase the available computation power for faster application processing or task resolution. Typically, parallel computing infrastructure is housed within a single facility where many processors are installed in a server rack or separate servers are connected together. The application server sends a computation or processing request that is distributed in small chunks or components, which are concurrently executed on each processor/server. Parallel computation can be classified as bit-level, instructional level, data, and task parallelism.
Types of parallel computing
From the open-source and proprietary parallel computing vendors, there are generally three types of parallel computing available, which are discussed below:
- Bit-level parallelism: The form of parallel computing in which every task is dependent on processor word size. In terms of performing a task on large-sized data, it reduces the number of instructions the processor must execute. There is a need to split the operation into a series of instructions. For example, there is an 8-bit processor, and you want to do an operation on 16-bit numbers. First, it must operate the 8 lower-order bits and then the 8 higher-order bits. Therefore, two instructions are needed to execute the operation. The operation can be performed with one instruction by a 16-bit processor.
- Instruction-level parallelism: In a single CPU clock cycle, the processor decides in instruction-level parallelism how many instructions are implemented at the same time. For each clock cycle phase, a processor in instruction-level parallelism can have the ability to address that is less than one instruction. The software approach in instruction-level parallelism functions on static parallelism, where the computer decides which instructions to execute simultaneously.
- Task Parallelism: Task parallelism is the form of parallelism in which the tasks are decomposed into subtasks. Then, each subtask is allocated for execution. And, the execution of subtasks is performed concurrently by processors.
Fundamentals of Parallel Computer Style
Parallel computer style exists in a wide variety of parallel computers, categorized according to the level at which the hardware supports parallelism. Parallel computer architecture and programming techniques work together to effectively utilize these machines. The classes of parallel computer architectures include:
- Multi-core computing: A multi-core processor is a computer processor integrated circuit with two or more separate processing cores, each of which executes program instructions in parallel. Cores are integrated onto multiple dies in a single chip package or onto a single integrated circuit die and may implement architectures such as multithreading, superscalar, vector, or VLIW. Multi-core architectures are categorized as either homogeneous, which includes only identical cores, or heterogeneous, which includes cores that are not identical.
- Symmetric multiprocessing: multiprocessor computer hardware and software architecture in which two or more independent, homogeneous processors are controlled by a single operating system instance that treats all processors equally, and is connected to a single, shared main memory with full access to all common resources and devices. Each processor has a private cache memory, may be connected using on-chip mesh networks, and can work on any task no matter where the data for that task is located in memory.
- Distributed computing: Distributed system components are located on different networked computers that coordinate their actions by communicating via pure HTTP, RPC-like connectors, and message queues. Significant characteristics of distributed systems include independent failure of components and concurrency of components. Distributed programming is typically categorized as client-server, three-tier, n-tier, or peer-to-peer architectures. There is much overlap in distributed and parallel computing and the terms are sometimes used interchangeably.
- Massively parallel computing: refers to the use of numerous computers or computer processors to simultaneously execute a set of computations in parallel. One approach involves the grouping of several processors in a tightly structured, centralized computer cluster. Another approach is grid computing, in which many widely distributed computers work together and communicate via the Internet to solve a particular problem.
Other parallel computer architectures include specialized parallel computers, cluster computing, grid computing, vector processors, application-specific integrated circuits, general-purpose computing on graphics processing units (GPGPU), and reconfigurable computing with field-programmable gate arrays. Main memory in any parallel computer structure is either distributed memory or shared memory.
Advantages of parallel computing
The advantages of parallel computing are that computers can execute code more efficiently, which can save time and money by sorting through “big data” faster than ever. Parallel programming can also solve more complex problems, bringing more resources to the table. That helps with applications ranging from improving solar power to changing how the financial industry works.
- Parallel computing models the real world
The world around us isn’t serial. Things don’t happen one at a time, waiting for one event to finish before the next one starts. To crunch numbers on data points in weather, traffic, finance, industry, agriculture, oceans, ice caps, and healthcare, we need parallel computers.
- Saves time
Serial computing forces fast processors to do things inefficiently. It’s like using a Ferrari to drive 20 oranges from Maine to Boston, one at a time. No matter how fast that car can travel, it’s inefficient compared to grouping the deliveries into one trip.
- Saves money
By saving time, parallel computing makes things cheaper. The more efficient use of resources may seem negligible on a small scale. But when we scale up a system to billions of operations – bank software, for example – we see massive cost savings.
- Solve more complex or larger problems
Computing is maturing. With AI and big data, a single web app may process millions of transactions every second. Plus, “grand challenges” like securing cyberspace or making solar energy affordable will require petaFLOPS of computing resources . We’ll get there faster with parallel computing.
- Leverage remote resources
Human beings create 2.5 quintillion bytes of information per day . That’s the number 25 with 29 zeros. We can’t possibly crunch those numbers. Or can we? With parallel processing, multiple computers with several cores can sift through many times more real-time data than serial computers working on their own.
Disadvantages of parallel computing
- Programming to target Parallel architecture is a bit difficult but with proper understanding and practice, you are good to go.
- The use of parallel computing lets you solve computationally and data-intensive problems using multicore processors, but, sometimes this effect some of our control algorithms and does not give good results and this can also affect the convergence of the system due to the parallel option.
- The extra cost (i.e. increased execution time) incurred is due to data transfers, synchronization, communication, thread creation/destruction, etc. These costs can sometimes be quite large, and may actually exceed the gains due to parallelization.
- Various code tweaking has to be performed for different target architectures for improved performance.
- Better cooling technologies are required in the case of clusters.
- Power consumption is huge by the multi-core architectures.
- Parallel solutions are harder to implement, they’re harder to debug or prove correct, and they often perform worse than their serial counterparts due to communication and coordination overhead.
Applications of Parallel Computing
There are various applications of Parallel Computing, which are as follows:
- One of the primary applications of parallel computing is Databases and Data mining.
- The real-time simulation of systems is another use of parallel computing.
- The technologies, such as Networked videos and Multimedia.
- Science and Engineering.
- Collaborative work environments.
- The concept of parallel computing is used by augmented reality, advanced graphics, and virtual reality.
Why parallel computing?
There are various reasons why we need parallel computing, such are discussed below:
- Parallel computing deals with larger problems. In the real world, there are multiple things that run at a certain time but at numerous places simultaneously, which is difficult to manage. In this case, parallel computing helps to manage this kind of extensively huge data.
- Parallel computing is the key to making data more modeling, dynamic simulation, and for achieving the same. Therefore, parallel computing is needed for the real world too.
- With the help of serial computing, parallel computing is not ideal to implement real-time systems; also, it offers concurrency and saves time and money.
- Only the concept of parallel computing can organize large datasets, complex, and their management.
- The parallel computing approach provides surety of the use of resources effectively and guarantees the effective use of hardware, whereas only some parts of hardware are used in serial computation, and some parts are rendered idle.
In conclusion, parallel computing plays a vital role in linking the world with each other more than before. Additionally, parallel computing’s approach becomes more necessary with multi-processor computers, faster networks, and distributed systems.