Concurrent processing is a computing model in which multiple processors execute instructions simultaneously for better performance.
Concurrent means something that happens at the same time as something else. Tasks are broken down into subtasks that are then assigned to separate processors to perform simultaneously, instead of sequentially as they would have to be carried out by a single processor. Concurrent processing is sometimes said to be synonymous with parallel processing.----------------------------------------------
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Parallel computing is a form of computation in which many calculations are carried out simultaneously,[1] operating on the principle that large problems can often be divided into smaller ones, which are then solved concurrently ("in parallel").
There are several different forms of parallel computing: bit-level, instruction level, data, and task parallelism. Parallelism has been employed for many years, mainly in high-performance computing, but interest in it has grown lately due to the physical constraints preventing frequency scaling.[2] As power consumption (and consequently heat generation) by computers has become a concern in recent years,[3] parallel computing has become the dominant paradigm in computer architecture, mainly in the form of multicore processors.[4]
Parallel computers can be roughly classified according to the level at which the hardware supports parallelism—with multi-core and multi-processor computers having multiple processing elements within a single machine, while clusters, MPPs, and grids use multiple computers to work on the same task. Specialized parallel computer architectures are sometimes used alongside traditional processors, for accelerating specific tasks.
Parallel computer programs are more difficult to write than sequential ones,[5] because concurrency introduces several new classes of potential software bugs, of which race conditions are the most common.
Communication and synchronization between the different subtasks are typically one of the greatest obstacles to getting good parallel program performance.
The maximum possible speed-up of a program as a result of parallelization is observed as Amdahl's law.
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