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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 ...
Applications for this architecture are much less common than MIMD and SIMD, as the latter two are often more appropriate for common data parallel techniques. Specifically, they allow better scaling and use of computational resources. However, one prominent example of MISD in computing are the Space Shuttle flight control computers. [2]
An example of a fine-grained system (from outside the parallel computing domain) is the system of neurons in our brain. [4] Connection Machine (CM-2) and J-Machine are examples of fine-grain parallel computers that have grain size in the range of 4-5 μs. [1]
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. (For example, consider a case where an 8-bit processor must add two 16-bit integers. The processor must first add the 8 lower-order bits from each integer, then ...
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.
It was the basis for Intel and HP development of the Intel Itanium architecture, [3] and HP later asserted that "EPIC" was merely an old term for the Itanium architecture. [4] EPIC permits microprocessors to execute software instructions in parallel by using the compiler, rather than complex on-die circuitry, to control parallel instruction ...
The opposite of embarrassingly parallel problems are inherently serial problems, which cannot be parallelized at all. A common example of an embarrassingly parallel problem is 3D video rendering handled by a graphics processing unit, where each frame (forward method) or pixel (ray tracing method) can be handled with no interdependency. [3]
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.