Parallel processing is defined as an architecture where processes are divided into separate segments, each executed simultaneously. By running processes on multiple processor cores instead of a single core, the time required to complete tasks is significantly reduced. The primary goal of parallel computing is to ensure that complex tasks are broken down into simpler steps for easier processing, enhancing performance and problem-solving capabilities.
Different segments of the processes are executed on multiple processors, with these segments communicating through shared memory. Once the various processes are executed and completed, they are combined at the end to provide a unified solution.
Parallel processing represents a shift in traditional computing. When tasks became more complex and the processing time for these tasks grew excessively long, traditional computing hit a wall. Moreover, such tasks often consume more energy and face communication issues and poor scalability. To overcome these challenges, parallel processing was developed, enabling process completion through the use of multiple cores.
Parallel processing forms a core concept for various machine learning algorithms and AI platforms. Traditionally, ML/AI algorithms were executed on single-processor environments, leading to performance bottlenecks. However, with the advent of parallel computing, data science and machine learning platform users can take advantage of concurrently running threads to manage various processes and tasks.
Types of Parallel Processing
Based on open-source or proprietary models, parallel computing is categorized into four types as listed below:
Bit-Level Parallelism: In this type of parallel computing, the word size of the processor is increased. Processes have fewer instruction sets to perform operations on variables larger than the processor’s word size.
Instruction-Level Parallelism: Here, hardware or software determines different runtime instructions. For example:
Hardware perspective: The processor determines the runtime for various instructions and which instructions should execute in parallel.
Software perspective: The software or compiler decides which instructions should work in parallel to ensure maximum efficiency.
Task Parallelism: Multiple distinct tasks are executed simultaneously. Typically, these tasks all access the same data to ensure minimal delay and smooth performance.
Superword-Level Parallelism: This type uses inline code to create different tasks for simultaneous execution.
Advantages of Parallel Processing
Some advantages of parallel processing include:
Overall Savings: Parallel processing helps users save time and costs. The time to execute a task is significantly greater when compared to executing tasks simultaneously across multiple processors. Besides time savings, cost savings is a key benefit, as it ensures efficient resource utilization. While it may be costly on a small scale, managing billions of operations simultaneously drastically reduces expenses.
Dynamic Nature: Solving real-world problems and finding efficient solutions increasingly relies on dynamic simulation and modeling to ensure simultaneous access to diverse data points. Parallel processing provides concurrency benefits, supporting the dynamic nature of various problems.
Optimal Resource Utilization: In traditional processing, there is a chance that not all hardware or software resources are utilized, leaving others idle. However, in parallel processing, tasks are divided and executed separately, making much greater use of hardware capacity to ensure faster processing times.
Managing Complex Data Sets: As data evolves and grows, ensuring its cleanliness and usability becomes challenging. Traditional processing may not be the best approach for managing large, unstructured, and complex data sets.
Impacts of Parallel Processing
Some major impacts of parallel processing include:
Supercomputer Capabilities: One key benefit of parallel computing is that it enables supercomputers to solve highly complex tasks in a fraction of the time. Supercomputers operate on the principle of parallel computing by dividing a highly complex task into smaller tasks and working on those smaller tasks. Parallel processing allows supercomputers to tackle significant problems such as climate change, testing healthcare models, space exploration, cryptography, chemistry, and many other fields.
Cross-Industry Advantages: Parallel processing will impact nearly all industries, from cybersecurity to healthcare, retail, and more. By developing algorithms tailored to industry-specific problems, parallel processing paves the way for faster processing times, helping industries understand benefits, costs, and limitations.
Big Data Support: As data volume expands across industries, managing large data sets becomes increasingly challenging. Parallel processing is poised to impact the explosion of big data significantly, as it shortens the time required for companies to handle these data sets. Furthermore, a mix of structured and unstructured data necessitates a higher level of computation to process massive volumes effectively—where parallel processing will play a pivotal role.
Parallel Processing vs. Serial Processing
Serial processing is defined as a type of processing in which tasks are completed sequentially. Tasks are completed one after another instead of simultaneously, as in parallel processing. Major differences between serial and parallel processing include:
Serial processing uses a single processor, whereas parallel processing uses multiple processors.
In serial processing, since only one processor is used, the workload on the processor is much higher compared to parallel processing.
Serial processing requires more time to complete various tasks as they are executed one after the other, whereas parallel processing completes tasks simultaneously.
