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Double Loops Loop Nests The next logical step in dependence analysis of loops is to extend the described concepts to double and multiple nested loops Example 7 displays a double loop over the indices i and j, containing the two statements, or tasks, S and T in the kernel Example 7 Double Loop for i = 0 to 5 do for j = 0 to 5 do S: A(i+1,j) = B(i,j) + C(i,j) T: B(i+1,j+1) = A(i,j) + 1 end for end for Suitable for the double loop, the arrays used in the tasks are two dimensional, whereby the index variable i of the outer loop is used only in the subscripts of the arrays rst dimension and the index variable j only in the subscripts of the second dimension While this is common practice in nested loops, it is neither a guaranteed nor a necessary condition for the dependence analysis in nested loops The arrays, for example, might only have one dimension, and the subscripts might be functions of more than one index variable Relevant for a dependence relation is only the reference of two different tasks to the same array element What the index variable is to the single loop is now, in a straightforward generalization, an index vector of two dimensions An instance of the double loop kernel (ie, an iteration) is determined by the two corresponding values of the index variables i and j Also, an instance of one of the tasks S and T is denoted by S(i, j) and T(i, j), respectively The extension to a more general nest of loops follows a similar pattern every loop simply contributes one dimension to the index vector In the same way, the index variable of a single loop can be treated as an index vector of one dimension By examining he tasks of the loop in Example 7, it becomes apparent that instance S(i + 1, j + 1) depends on instance T(i, j), caused by the references to the elements of array B, and instance T(i + 1, j) depends on S(i, j), caused by the references to the elements of array A As a logical consequence of the generalization from the index variable to an index vector, the dependence distance is also expressed as a distance vector For the identi ed dependence relations in Example 7, the distance vectors are (1, 1), for S(i + 1, j + 1) depending on T(i, j), and (1, 0), for T(i + 1, j) depending on S(i, j) So there are two uniform dependences, as the distance vector is constant for every dependence The determination of the dependence relations and the distance vectors for the loops in Example 5 and Example 7 are relatively simple In real programs, however, various circumstances can make dependence analysis more complicated and time consuming Sometimes it might even be impossible to determine the dependence relation of a program: for example, when a subscript of an array, which is read and written in various tasks, is a function of an input variable of the program In that case, the dependence relations can only be established at runtime A conservative approach,.