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33 DEPENDENCE GRAPH (DG) Recall the discussion of dependence in Section 25, where directed dependence relations are analyzed between tasks Intuitively, one can represent the programs tasks as nodes and the dependence relations as edges, corresponding to the graph model de ned in the previous section Consider the program in Example 1 of Section 25, which contains ow dependence relations Representing every task, in this case every statement, by a node and every dependence relation by an edge directed from the cause to the dependent task, a graph re ecting the dependence structure is obtained Figure 37 repeats the program code of Example 1 and epicts the corresponding dependence graph From this graph one intuitively perceives that tasks 2 and 3 can be executed concurrently Next, the dependence graph is de ned formally De nition 310 (Dependence Graph (DG)) A dependence graph is a directed graph DG = (V, E) representing a program P according to the graph model of De nition 37 The nodes in V represent the tasks of P and the edges in E the dependence relations between the tasks An edge eij E from node ni to nj , ni , nj V, represents the dependence of node nj on node ni . 128 Creator In VB.NET Using Barcode printer for .NET .Related: Make EAN-13 C# , ITF-14 Generator .NET , Printing Code 39 ASP.NET

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iteration DG cannot be constructed This fact is noteworthy, especially in conjunction with the observation made at the end of Section 252: the known techniques of dependence analysis can only compute in reasonable time the distance vectors for dependence relations based on simple, albeit common, types of subscript functions Not to forget that it is sometimes even impossible to determine the dependence relations, for example, when array ubscripts are functions of input variables of the program The only solution is then to create a dependence edge for every possible dependence relation in order to obtain a conservative dependence graph, that is, a graph that includes all edges that the true dependence graph would have 332 Summary As the name indicates, the dependence graph is a representation of the dependence structure of a program In its general form, as in De nition 310, the DG is not bound to any particular computation type or granularity This general theoretical form is widely employed, whenever it is necessary to reason about the dependence structure of a program, the feasibility of computations, or the validity of program transformations (see the literature referenced in Section 251) In practical usage, the DG is limited to a coarse grained or partial representation of a program, because its size (and with it its computer representation) grows with the number of tasks The dependence structure of cyclic computations is represented by the iteration dependence graph, where every node of the graph is associated with coordinates of the iteration space spanned by the index variables of the loop nest For dependence analysis of loops and, above all, for loop transformations, the iteration DG is a valuable instrument Banerjee et al [17] survey many of the existing techniques Although several of them do not require the explicit construction of the graph, the underlying theory of these techniques is based on the dependence graph Granularity in loops is typically small to medium, which is therefore the common granularity of iteration dependence graphs The size issue of the general DG is evaded by bene tting from the regular iterative structure of the computation A compact computer representation, given that the dependence distances are uniform, comprises only the tasks of the loop kernel, the intraiteration dependence relations, and the distance vectors of the interiteration dependence relations Iteration DGs are employed not only in parallelizing compilers but also in VLSI array processor design (Kung [109]) There, the iteration DG, constructed from an initial application speci cation typically signal processing serves as an intermediate representation for the mapping and scheduling of the application to array processors Those graphs possess two distinctive attributes: (1) a node represents one iteration, not only a part of it; and (2) the dependence relations are local, that is, the absolute values of the distance vectors elements are typically 1 or 0 (Karp et al [101]) The mapping and scheduling process transforms the iteration DG into a ow graph, which is treated in the next section While the dependence graph has no af nity for any particular parallel architecture, the iteration dependence graph, owing to its regular iterative structure, is usually employed for homogeneous systems, with SIMD or SPMD streams.

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