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Using the For( ) Method
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One way that the TPL supports data parallelism is through the For( ) method defined by Parallel The are several forms of For( ) We will start with the simplest version, which is shown here: public static ParallelLoopResult For(int fromInclusive, int toExclusive, Action<int> body) Here, fromInclusive specifies the starting value of what corresponds to the loop control variable (also called the iteration value or index value), and toExclusive specifies one greater than the ending value Each time through the loop, the loop control variable will increase by one Thus, the loop will iterate from fromInclusive to toExclusive 1 The code that will be iterated is specified by the method passed to body This method must be compatible with the Action<int> delegate, which is shown here: public delegate void Action<in T>(T obj) Of course, in the case of For( ), T must be int The value passed to obj will be the next value of the loop control value The method passed to body can be a named or anonymous method For( ) returns a ParallelLoopResult instance that describes the completion status of the loop For simple loops, this value can be ignored (We will look at this value closely a bit later)
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The key point about For( ) is that it can (when feasible) parallelize the loop code This can, in turn, lead to performance improvement For example, in a loop that applies a transformation to an array, the process can be broken into pieces to allow different portions of the array to be transformed simultaneously Understand, however, that no performance boost is guaranteed because of differences in the number of available processors in different execution environments, and because parallelizing small loops may create more overhead than the time that is saved The following shows a simple example of For( ) It begins by creating an array called data that contains 1,000,000,000 integers It then calls For( ), passing as the loop body a method called MyTransform( ) This method contains a number of statements that perform arbitrary transformations on the data array Its purpose is to simulate an actual operation As explained in greater detail in a moment, for data parallelism to be effective, the operation being performed must usually be non-trivial If it isn t, then a sequential loop can be faster
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// Use ParallelFor() to create a data-parallel loop using System; using SystemThreadingTasks; class DemoParallelFor { static int[] data; // A method to be run as the body of a parallel loop // The statements in this loop are designed to simply // consume some CPU time for the purposes of demonstration static void MyTransform(int i) { data[i] = data[i] / 10; if(data[i] if(data[i] if(data[i] if(data[i] } static void Main() { ConsoleWriteLine("Main thread starting"); data = new int[100000000]; // Initialize the data using a regular for loop for(int i=0; i < dataLength; i++) data[i] = i; // A parallel For loop ParallelFor(0, dataLength, MyTransform); ConsoleWriteLine("Main thread ending"); } } < > > > 10000) data[i] = 0; 10000 & data[i] < 20000) data[i] = 100; 20000 & data[i] < 30000) data[i] = 200; 30000) data[i] = 300;
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The program contains two loops The first is a standard for loop that initializes data The second is a parallel For( ) loop that applies a transformation to each element in data As
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