Parallel computing is general purpose because there is a single unifying mechanism on which it is based---this is called domain decomposition or data parallelism. Nature solves complex problems by dividing them up and assigning particular neurons, or group of neurons, to different parts of the problem. This is illustrated in Figure 4, which shows that different areas of the brain are responsible for disentangling tactile information from different parts of the body. Again, vision is a major task for the brain and there is direct spatial mapping of received pixels of light at the retina to neurons in the brain.
Figure 4: Three Parallel Computing Strategies Found in the Brain (of a
Rat). Each figure depicts brain activity corresponding to various
functions: (A) continuous map of a tactile inputs in somatosensory
cortex, (B) patchy map of tactile inputs to cerebellar cortex, and (C)
scattered mapping of olfactory cortex as represented by the
unstructured pattern of 2DG update in a single section of this cortex
[Nelson:90b].
Parallel simulation of interacting particles, shown in Figure 5, is handled by data parallelism with individual particles being assigned to a particular node in the parallel machine. The astrophysical simulation of Figure 5 is very inhomogeneous and corresponding the spatial regions assigned to a node are irregular and indeed time dependent. This complexity was challenging for the implementation, but the resultant program achieved excellent performance with a speedup of over 800 on a 1024-node nCUBE. Similar success can be anticipated for parallel implementations of molecular dynamics codes, such as CHARMM.
Figure 5: A Two-dimensional Projection of a Model Universe in which Two
Galaxies are on a Collision Course. This is a simplified version with
18,000 ``stars'' of a large simulation reported in [Salmon:89b]. The
irregular decomposition onto a 16-node machine is illustrated above.
Several chemistry computations involve generation and manipulation of large full matrices that represent the interaction Hamiltonian. Energy level calculations involve eigenvalue determination while scattering can use matrix multiplication and linear equation solution. The same concept of data parallelism is used with, as seen in Figure 6, a simple regular decomposition of the matrix onto the processors. Parallelism is present both for generation of the matrix elements that proceed independently in each node, and the eigenvalue and other matrix operations. A general matrix library LAPACK will soon be available for a broad class of high-performance vector and parallel computers.
Figure 6: 
Matrix Decomposed onto a 
Parallel
Computer Array
Problems consist of algorithms applied to a large data domain. Data parallelism achieves parallelism by splitting up domain and applying the computational algorithm concurrently to each point.