A SIMD computer consists of n identical processors, each with their own local memory, where it is possible to store data. All processors work under the control of a single instruction stream. In addition to this, there are n data streams, one for each processor. The processors work simultaneously on each step and execute the same instructions, but on different data elements. This is an example of data-level parallelism.

The SIMD architectures are much more versatile than MISD architectures. Numerous problems covering a wide range of applications can be solved by parallel algorithms on SIMD computers. Another interesting feature is that the algorithms for these computers are relatively easy to design, analyze, and implement. The limitation is that only the problems that can be divided into a number of subproblems (which are all identical, each of which will then be solved simultaneously through the same set of instructions) can be addressed with the SIMD computer.

With the supercomputer developed according to this paradigm, we must mention the Connection Machine (Thinking Machine, 1985) and MPP (NASA, 1983).

As we will see in Chapter 6, Distributed Python, and Chapter 7, Cloud Computing, the advent of modern graphics cards (GPUs), built with many SIMD-embedded units, has led to the more widespread use of this computational paradigm.