Parallel computing has a major design difference with the earlier concurrent computing. Typically, it reduces the execution cycle/timing by taking advantage of the infrastructure/hardware ability to execute/complete more than one task at any point of given time.

Parallel computing leverages various techniques, such as vectorisation, instruction level parallelism named super scalar architecture, multiprocessing using multiple core processors, and so on. At a software level, there is another model in which uniform operations over aggregate data can be speed up. This is achievable by partitioning the data and computing on the partitions simultaneously.

Parallelism is a combination of software and hardware techniques. It is used to allow several processes to run in parallel, physically. The primary objective is used for faster computation in modern enterprise applications. Take a look at the following diagram:

In a nutshell, parallelism achieves the physical execution of multiple tasks at once using multiple processors in the ecosystem.