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Explicitly parallel instruction computing (EPIC) is a term coined in 1997 by the HP–Intel alliance [1] to describe a computing paradigm that researchers had been investigating since the early 1980s. [2] This paradigm is also called Independence architectures.
Parallel computing is a type of computation in which many calculations or processes are carried out simultaneously. [1] Large problems can often be divided into smaller ones, which can then be solved at the same time. There are several different forms of parallel computing: bit-level, instruction-level, data, and task parallelism.
Data-intensive computing is a class of parallel computing applications which use a data parallel approach to process large volumes of data typically terabytes or petabytes in size and typically referred to as big data.
Task parallelism (also known as function parallelism and control parallelism) is a form of parallelization of computer code across multiple processors in parallel computing environments. Task parallelism focuses on distributing tasks—concurrently performed by processes or threads—across different processors.
Loop-level parallelism is a form of parallelism in software programming that is concerned with extracting parallel tasks from loops.The opportunity for loop-level parallelism often arises in computing programs where data is stored in random access data structures.
A skilled parallel programmer may take advantage of explicit parallelism to produce efficient code for a given target computation environment. However, programming with explicit parallelism is often difficult, especially for non-computing specialists, because of the extra work and skill involved in developing it.
Atanasoff–Berry computer, the first computer with parallel processing [1] Instruction-level parallelism (ILP) is the parallel or simultaneous execution of a sequence of instructions in a computer program. More specifically, ILP refers to the average number of instructions run per step of this parallel execution. [2]: 5