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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.
Granularity is closely tied to the level of processing. A program can be broken down into 4 levels of parallelism - Instruction level. Loop level; Sub-routine level and; Program-level; The highest amount of parallelism is achieved at instruction level, followed by loop-level parallelism. At instruction and loop level, fine-grained parallelism ...
Data parallelism is parallelization across multiple processors in parallel computing environments. It focuses on distributing the data across different nodes, which operate on the data in parallel. It can be applied on regular data structures like arrays and matrices by working on each element in parallel.
The degree of parallelism (DOP) is a metric which indicates how many operations can be or are being simultaneously executed by a computer. It is used as an indicator of the complexity of algorithms, and is especially useful for describing the performance of parallel programs and multi-processor systems.
Bit-level parallelism is a form of parallel computing based on increasing processor word size.Increasing the word size reduces the number of instructions the processor must execute in order to perform an operation on variables whose sizes are greater than the length of the word.
In computing, a parallel programming model is an abstraction of parallel computer architecture, with which it is convenient to express algorithms and their composition in programs. The value of a programming model can be judged on its generality : how well a range of different problems can be expressed for a variety of different architectures ...
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.
"Embarrassingly" is used here to refer to parallelization problems which are "embarrassingly easy". [4] The term may imply embarrassment on the part of developers or compilers: "Because so many important problems remain unsolved mainly due to their intrinsic computational complexity, it would be embarrassing not to develop parallel implementations of polynomial homotopy continuation methods."