What is parallel processing?
Parallel processing is defined as an architecture where processes are split into separate parts and each part is run simultaneously. By running the processes on multiple processor cores instead of a single one, the time taken to execute tasks is much lower. The main goal of parallel computing is to ensure that complex tasks are broken into simpler steps for easier processing driving better performance and problem-solving capabilities.
Different parts of the processes run on multiple processors, and these various parts communicate via shared memory. Once the various processes are run and completed, they are combined at the end to provide a single solution.
Parallel processing is an evolution to traditional computing. Traditional computing hit a wall when tasks were getting more complex and the processing times for these tasks would take very long. In addition, such tasks often consume more power and have poor communication and scaling issues. To prevent such issues, parallel processing was created to tackle them and, at the same time, ensure that processes were completed by using multiple cores.
Parallel processing forms a core concept for several machine learning algorithms and AI platforms. ML/AI algorithms were run traditionally on single processor environments, which led to performance bottlenecks. The introduction of parallel computing, however, allows users of data science and machine learning platforms to exploit the simultaneously executing threads that handle different processes and tasks.
Types of parallel processing
Depending on proprietary or open source, parallel computing has four different types listed below:
- Bit-level parallelism: In this type of parallel computing, the processor word size is increased. The processes will have a lesser instruction set to perform operations on variables whose size is greater than the processor word size.
- Instruction-level parallelism: In this type of parallel computing, the controlling hardware or software will decide different run-time instructions. For example, from a hardware perspective, the processor decides the run time for different instructions and which instruction needs to execute in parallel. From a software perspective, the software or compiler will decide which instructions need to work parallelly to ensure maximum performance.
- Task parallelism: Several different tasks are run at the same time. Usually, these different tasks all have access to the same data to ensure no delays and smooth performance.
- Superword-level parallelism: This type of parallelism uses inline code to create different tasks to run simultaneously.
Benefits of using parallel processing
A few benefits of parallel processing include:
- Overall savings: Parallel processing helps users save on time and costs. The time to run one task is extremely high compared to running the same task on different processors at once. In addition to time savings, cost savings are a key benefit since it makes efficient use of resources. Although on a small scale it is expensive, managing billions of operations simultaneously reduces expenses significantly.
- Dynamic nature: To solve more real-world problems and find efficient solutions, it is becoming increasingly important to focus on dynamic simulation and modeling to ensure different data points are available concurrently. Parallel processing offers the benefit of concurrency thereby supporting the dynamic nature of several problems.
- Optimized resource utilization: In classical, traditional processing there is a possibility that not the entire hardware or software is being utilized while the rest remain idle. However, in the case of parallel processing, since the tasks are decoupled and run separately, the hardware is utilized much more in capacity to ensure faster processing times.
- Managing complex data sets: As data evolves and grows, it is hard to ensure that data remains clean and usable. Data sets are becoming more complex, and traditional processing might not be the best way forward for managing large, unstructured, and complex data sets.
Impacts of using parallel processing
Some of the main impacts of parallel processing include:
- Supercomputing capabilities: One of the key advantages of using parallel computing is it helps supercomputers solve highly complex tasks in a fraction of the time. Supercomputers are machines that work on the principle of parallel computing, by splitting a highly complex task into smaller ones and working on those smaller tasks. The ability of parallel processing helps supercomputers to work on several important problems such as climate change, testing models for healthcare, space, cryptology, chemistry, and numerous other fields.
- Cross-functional vertical benefits: Parallel processing will have an impact on almost all industries, from cybersecurity to healthcare to retail and several others. By developing algorithms related to the problems faced by various industries, parallel processing provides the avenue for faster processing time and helps understand the benefits, costs, and limitations across industries.
- Big data support: As the amount of data keeps expanding across numerous industries, it becomes increasingly difficult to manage these large data sets. Parallel processing is set to impact the big data explosion since it would shorten the time significantly for companies and enterprises to manage these data sets. In addition, the mix of structured and unstructured data will require a higher type of computing to process the massive amount of data—parallel processing will have a key impact here.
Parallel processing vs. serial processing
Serial processing is defined as the type of processing in which tasks are completed in a sequential order. Tasks are completed one at a time, instead of side by side as in the case of parallel processing. Some of the major differences between serial and parallel processing are as follows:
- Serial processing uses a single processor whereas parallel processing uses multiple processors
- Since there is only one processor in serial processing, the workload that is being processed is much higher by the one processor which is not the case in parallel processing
- Serial processing takes more time to complete various tasks since they are completed one after the other whereas in parallel processing tasks are completed simultaneously
Preethica Furtado
Preethica is a Market Research Manager at G2 focused on the cybersecurity, privacy and ERP space. Prior to joining G2, Preethica spent three years in market research for enterprise systems, cloud forecasting, and workstations. She has written research reports for both the semiconductor and telecommunication industries. Her interest in technology led her to combine that with building a challenging career. She enjoys reading, writing blogs and poems, and traveling in her free time.
