Final answer:
Parallel processing approaches used in computers include SIMD, MIMD, and GPU computing. SIMD executes the same instruction on multiple data elements, MIMD allows different instructions on different data sets, and GPU computing utilizes parallel processing on GPUs for computational tasks.
Step-by-step explanation:
Parallel processing refers to the simultaneous execution of multiple tasks or instructions by dividing them into smaller subtasks that can be executed simultaneously. There are several parallel processing approaches used in computers:
1. SIMD (Single Instruction, Multiple Data)
With SIMD, the same instruction is executed simultaneously on multiple data elements. It is efficient for tasks that involve performing the same operation on multiple sets of data, such as image and video processing. One example of SIMD architecture is Intel's Streaming SIMD Extensions (SSE) instructions.
2. MIMD (Multiple Instruction, Multiple Data)
MIMD allows different instructions to be executed simultaneously on different sets of data. It is commonly used in multiprocessor systems, where each processor can execute independent instructions and access its own set of data. An example of MIMD architecture is the Message Passing Interface (MPI) used in distributed computing.
3. GPU (Graphics Processing Unit) Computing
GPUs are highly parallel processors designed to handle massive computational tasks. In GPU computing, the workload is divided into smaller tasks, and each task is assigned to different GPU cores for simultaneous execution. This approach is particularly used in applications that require intensive parallel processing, such as graphics rendering and machine learning.