One way to speed up some types of algorithms is to use parallel computing to spread the algorithm across multiple processors running simultaneously.
What is Parallel Computing?
Parallel computing refers to the process of breaking down larger problems into smaller, independent, often similar parts that can be executed simultaneously by multiple processors communicating via shared memory, the results of which are combined upon completion as part of an overall algorithm. Its primary goal is to increase available computation power for faster application processing and problem-solving.
Parallel computing infrastructure is typically housed within a single data center where several processors are installed in a server rack; computation requests are distributed in small chunks by the application server that is then executed simultaneously on each server.
Why do you need it?
A traditional method of computation involves performing software operations on a single computer (with a single central processing unit). The CPU uses a series of instructions to solve the problem in sequence, but only one of the instructions can be executed at a time. Parallel computing is an evolution of this sequence calculation method, with an intention to simulate the state of the natural world, where events occur simultaneously and can be processed in parallel at the same time. According to this principle, the processing speed of complex instructions can be accelerated, and computation time can be reduced.
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.
- In parallel computing, more resources are used to complete the task which led to a decrease the time and cuts possible costs. Also, cheap components are used to construct parallel clusters.
- Compared with Serial Computing, parallel computing can solve larger problems in a short time.
- For simulating, modeling, and understanding complex, real-world phenomena, parallel computing is much more appropriate when compared with serial computing.
- When the local resources are finite, they can offer benefit you over non-local resources.
- There are multiple problems that are very large and may be impractical or impossible to solve on a single computer; the concept of parallel computing helps to remove these kinds of issues.
- One of the best advantages of parallel computing is that it allows you to do several things at a time by using multiple computing resources.
- Furthermore, parallel computing is suited for hardware as serial computing wastes the potential computing power.
- It addresses such communication and synchronization between multiple sub-tasks and processes which is difficult to achieve.
- The algorithms must be managed in such a way that they can be handled in a parallel mechanism.
- The algorithms or programs must have low coupling and high cohesion. But it’s difficult to create such programs.
- More technically skilled and expert programmers can code a parallelism-based program well.
Future of Parallel Computing
The computational graph has undergone a great transition from serial computing to parallel computing. Tech giant such as Intel has already taken a step toward parallel computing by employing multicore processors. Parallel computation will revolutionize the way computers work in the future, for the better good. With all the world connecting to each other even more than before, Parallel Computing does a better role in helping us stay that way. With faster networks, distributed systems, and multi-processor computers, it becomes even more necessary.