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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 the asks 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,.

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ADO.NET Feature ADO standards support the feature to share . Reading UPCA In VS .NET Using Barcode decoder for . Applications use the OleDbCommand class to create select, insert update, and delete commands that need to be sent to the data source. Also, this class can be used to execute stored procedures besides sending input parameters to the stored procedure and retrieving output parameters from the stored procedure. Here is sample code that inserts data using an insert command into an Access table: <%@ Page Language="VB"%> <%@ Import Namespace="System.Data.OleDb"%.Related: 

dynamic reducts, 100 102 evaluation, 106 111 PASH, 106 Preset, 106 QuickReduct algorithm, 87 88 proof of monotonicity, 90 relative dependency, 102 103 RSAR, 86 algae population estimation, 126 128 applications, 113 medical image classi cation, 113 117 optimizations, 91 95 text categorization, 117 126 RSAR-SAT, 279 283 tolerance approach, 103, 275 approximations, 104 similarity, 103 tolerance QuickReduct, 104 105 variable precision rough set theory, 98 100 Rough set theory, 25, 133 applications, 128 132 bioinformatics and medicine, 129 130 fault diagnosis, 130 nancial investment, 129 music and acoustics, 131 132 pattern classi cation, 131 prediction of business failure, 128 approximations, 28 boundary region, 28 core, 31 degree of dependency, 29 discernibility function, 32, 280 281 discernibility matrix, 31, 280 281 feature selection, see Rough set-based feature selection indiscernibility 27 introduction, 25 motivations, 4 5 negative region, 28 positive region, 28. Barcode Creation In .NET Using Barcode generation for . NET Control to generate, create bar code mage in VS .NET applications.Related: 

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