6. Parallel Processing

There are a number of ways to carry out parallel processing, The table below shows each one of them and how they are applied in real life.

Types of parallel processing	Class of computer	Application
Pipeline	Single Instruction Single Data (SISD)	Inside a CPU
Array Processor	Single Instruction Multiple Data SIMD	Graphics cards, games consoles
Multi-Core	Multiple Instruction Multiple Data MIMD	Super computers, modern multi-core chips
Co-processor	Separate processor	Floating point maths

Advantages of parallel processing over the Von Neumann architecture

Faster when handling large amounts of data, with each data set requiring the same processing (array and multi-core methods)
Is not limited by the bus transfer rate (the Von Neumann bottleneck)
Can make maximum use of the CPU (pipeline method) in spite of the bottleneck

Disadvantages

Only certain types of data are suitable for parallel processing. Data that relies on the result of a previous operation cannot be made parallel. For parallel processing, each data set must be independent of each other.
More costly in terms of hardware - multiple processing blocks needed, this applies to all three methods

Challenge see if you can find out one extra fact on this topic that we haven't already told you

Click on this link: Parallel processing