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such a problem for parallel execution, an initial search tree is generated until there is a suf ciently large number of leaf nodes that can be executed in parallel (ie, until the required degree of concurrency is met) Each of these leaf nodes corresponds to an independent task, which searches for a solution in the subtree induced below this node The overall computation can nish as soon as one of the parallel tasks nds a solution This fact is the main difference between exploratory decomposition and data decomposition, where all subproblems must be solved in their entirety Speculative Decomposition Speculative decomposition takes the idea of exploratory decomposition even one step further In applications where the choice for a certain computation branch depends on the result of an earlier step, a speedup can be achieved by speculatively executing all (or a subset of all) branches At the time the data for making the choice is nally available, only the result of the correct branch is chosen; the others are discarded For example, an if statement in a programming language has wo lines of execution one if the evaluated expression is true, another if it is false In speculative decomposition, each of the two branches is made a task and executed concurrently At the time the expression of the if statement can nally be evaluated, the result of the correct branch is chosen and the result of the other branch is discarded 243 Computation Type and Program Formulation Essential for the subtask decomposition is the granularity, the dependence structure, and the regularity inherent in an application and its description as a program The computation type of an application and its expression as a program may already give a strong indication for its decomposition into subtasks Computation Type Most applications belonging to one area might employ certain types of computation and data structures For instance, an application of signal processing is likely to perform linear algebra on matrices In this case, data decomposition is probably the right technique Other application areas might typically use other types of data structures (eg, lists and graphs) and computations (eg, matching and search algorithms) For these application areas, recursive or exploratory decomposition can be a good candidate Furthermore, depending on the computation type, an application might consist of several distinct steps or one iterative block of computation In the former case, each step can be represented by one task, hence using coarse grained parallelism, while in the latter case the iterative computation suggests a regular, small grained decomposition, potentially based on data decomposition The type of the application also limits the inherent degree of concurrency For example, the matrix vector multiplication in Figure 216 performs n2 multiplications and n(n 1) additions All multiplications can be performed concurrently, but the additions must wait for the multiplications to nish Hence, the degree of concurrency is bounded by n2 Also, it is often pointless to partition an application into subtasks that strongly depend on each other s results, preventing their parallel execution due to the small degree of concurrency As will be shown in the next section,.



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