Parallel processing definition
Parallel processing is a computing technique that involves running two or more processors to handle separate parts of one task. Parallel processing breaks down large tasks into smaller sub-tasks, reducing the amount of time a program takes to run. Any system with more than one central processing unit (CPU) can perform parallel processing, including multi-core processors.
See also: throughput, unified computing system
How parallel processing works
- Breaking up the task. The task is broken down into smaller sub-tasks that can be performed simultaneously.
- Allocating resources. Each task is assigned to a different processor or distributed across multiple processors to optimize performance.
- Synchronization. When the resources are allocated, the processors perform the sub-tasks simultaneously. The results of each sub-task have to be synchronized to ensure that the final output is accurate and complete. Synchronization mechanisms like locks, barriers, and semaphores may be used.
- Combining results. When each sub-task is completed, the results are combined to produce the final output. This stage may involve aggregating the results of each sub-task and merging the outputs.
Benefits of parallel processing
- Faster processing times. Parallel processing allows systems to perform multiple sub-tasks, reducing the time required to complete a task.
- Increased throughput. Parallel processing may increase the overall throughput of a computing system.
- Cost-effectiveness. Using multiple processors can be more cost-effective than using one high-performance processor.
- Scalability. Parallel processing can be scaled up (or down) depending on the task requirements.
- Better fault tolerance. Parallel processing systems often are more fault-tolerant, meaning they can continue to operate even if one of the processors or computing units fails.