Parallel computation refers to a certain type of computing in which numerous mathematical executions are carried out all at once. With the power of parallel computing, it’s possible for many different, small executions to be made out of one massive problem.

Instead of burning the resources to tackle a large problem in its entirety, it can instead be broken down into a great many small computations that can be individually solved at an expedited rate. Parallel computing can be used to whittle down the difficulty and processing power of large problems into segmented sequences that can be carried out with comparative ease.

Types of parallelism

Parallelism is strongly tied to high-performance computing, and most recently, the interest in parallel computing has been based in the challenges of frequency scaling with physical constraints. Because of the effects of physical constraints on frequency scaling, parallel computing has grown more popular in recent times than before. The different forms of parallel computing can be broken down into the following forms: task, data, instruction-level and bit-level parallelism.

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Task parallelism

Task parallelism, referred to as control parallelism, has to do with computer code paralleling across multiple levels and different processors. With task parallelism, there are subsequent executions of multiple processes/threads on either different data sets or on the same data set; in either scenario, there may either be shared or unique code.

Data parallelism

Task parallelism contrasts data parallelism. Data parallelism is commonly used to take collections of data and methodically distribute them across multiple nodes. When data parallelism is in play, the elements of matrices and arrays can be can be operated on in parallel. All elements of a data parallel task can be even divided across all of the different processors with equal prioritization.

Instruction-level parallelism

The concept of instruction-level parallelism has to do with the exact quantity of simultaneously-executed instructions in a given computer program. No matter what the number of these different instructions may be, there will be two different types of approaches to the instructional variation of parallelism: the software approach and the hardware approach.

In the software approach to instruction-level parallelism, the focus is put upon static parallelism. In static parallelism, the computer itself is what determines the exact instructions that are to be carried out. In contrast to the static parallelism of the software approach, the hardware approach primarily concerns dynamic parallelism. In dynamic parallelism, the instructions put in queue for execution are determined by the processor’s run-time decision.

Bit-level parallelism

Bit-level processing was the primary factor of computer architecture development between 1970 and the mid 1980s. This form of parallelism has to do with the word size enlargement of a processor. By increasing the word size, the number of instructions that a processor needs to run before any given operation can be executed is reduced by a fair margin.


Parallel computation is oftentimes run alongside concurrent computing, though pairing the two together is not necessary. The degree to which any computer program can be accelerated with parallelism is referred to by Amdahl’s law.