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By Georgiev P., Pardalos P., Theis F.

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2-19 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Reflections on Models of Parallel Computation One important objective of models of computation [1] is to provide a framework for the design and the analysis of algorithms that can be executed efficiently on physical machines. In view of this objective, this chapter reviews the Decomposable Bulk Synchronous Parallel (D-BSP) model, introduced in Reference 2 as an extension to BSP [3], and further investigated in References 4–7.

1 Reflections on Models of Parallel Computation One important objective of models of computation [1] is to provide a framework for the design and the analysis of algorithms that can be executed efficiently on physical machines. In view of this objective, this chapter reviews the Decomposable Bulk Synchronous Parallel (D-BSP) model, introduced in Reference 2 as an extension to BSP [3], and further investigated in References 4–7. In the present section, we discuss a number of issues to be confronted when defining models for parallel computation and briefly outline some historical developments that have led to the formulation of D-BSP.

1 (|011 · · · 1 ± |100 · · · 0 ). 2 © 2008 by Taylor & Francis Group, LLC 1-17 Evolving Computational Systems These vectors form an orthonormal basis for the state space corresponding to the n-qubit system. In such superpositions, the n qubits forming the system are said to be entangled: Measuring any one of them causes the superposition to collapse into one of the two basis vectors contributing to the superposition. Any subsequent measurement of the remaining n − 1 qubits will agree with that basis vector to which the superposition collapsed.

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