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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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Spire.PDF has a function of adding, removing the blank pages in C#. We have already shown you how to remove the blank page in a PDF file. This article will ...

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Explains how to count PDF pages using regular expressions in C# . ... First, we need to open the PDF file using a FileStream and read the contents as a string ...

24:

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pages will need to be read from disk into memory which translates to a performance boost. Let s use another example to demonstrate. In this example, we ll select the average unit price from the Sales.SalesOrder_ Detail_Copy table. Again, this table was modified for the purposes of the test. For this example, the table was increased in size to 6.7 million rows. Given that the UnitPrice field isn t indexed, a full table scan will result, which is ideal for our test. We ll run this three times, on an uncompressed table, and with both forms of compression enabled. For this test, we ll clear the buffer cache with DBCC DROPCLEANBUFFERS before each test to ensure the query reads from disk each time. The script used for this test looks like this:

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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

// 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;

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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