Data Level Parallelism
# Data Level Parallelism
The same operation is performed on multiple data values concurrently in multiple processing units. Can reduce the Instruction Count to enhance performance
# Processors
Different types of hardware can support different levels of data parallelism.
# Flynn’s Processor Taxonomy
Advantages of SIMD > MIMD
- Allow sequential thinking yet achieves parallel speedup
- Reduced energy usage
- More efficient parallel efficiency
# Single Instruction Multiple Data (SIMD)
# Vector processor
One vector instruction can perform N computations, where N is the vector length.
- Reduces the number of instructions: less branching
- Less execution time with lower instruction count
- Simpler design as there is no requirement for data dependency check since each execution is independent
# Array processor
- Array processor works more like a true parallel system, with each processor able to run same instruction on different data
- Vector processor works in more of a pipelined fashion.
# Multimedia Extensions (MMX)
