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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

Part II:

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stated, in this case, the transformation is arbitrary (being used simply for demonstration) The For( ) automatically breaks up the calls to MyTransform( ) so they can be run on separate portions of data in parallel Therefore, if you run this program on a computer that has two or more available processors, the For( ) loop can run in parallel It is important to understand that not all loops will be more efficient when parallelized In general, small loops or loops that perform very simple operations are often faster as sequential rather than parallel loops This is why the for loop that initializes data is not a parallel For( ) The reason that small or very simple loops might not be efficient when parallelized is because the time needed to set up the parallel tasks and the time needed to context-switch exceeds the time saved by parallelization To prove this point, the following program creates both parallel and sequential versions of both for loops in the program and times each one for comparison purposes:

1 2

// Show timing differences between sequential and parallel for loops using System; using SystemThreadingTasks; using SystemDiagnostics; 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"); // Create a Stopwatch instance to time loops Stopwatch sw = new Stopwatch(); data = new int[100000000]; // Initialize data swStart(); // Parallel version of initialization loop ParallelFor(0, dataLength, (i) => data[i] = i ); swStop(); ConsoleWriteLine("Parallel initialization loop: swElapsedTotalSeconds); {0} secs", < > > > 1000) data[i] = 0; 1000 & data[i] < 2000) data[i] = 100; 2000 & data[i] < 3000) data[i] = 200; 3000) data[i] = 300;

24:

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swReset(); swStart(); // Sequential version of initialization loop for(int i=0; i < dataLength; i++) data[i] = i; swStop(); ConsoleWriteLine("Sequential initialization loop: {0} secs", swElapsedTotalSeconds); ConsoleWriteLine();

Our differentiation formulas for inverse trigonometric functions can be written in reverse, as antidifferentiation formulas We have du 1 du 1 u2 u2 = Sin

// Perform transforms swStart(); // Parallel version of transformation loop ParallelFor(0, dataLength, MyTransform); swStop(); ConsoleWriteLine("Parallel transform loop: swElapsedTotalSeconds); swReset(); swStart(); // Sequential version of transformation loop for(int i=0; i < dataLength; i++) MyTransform(i); swStop(); ConsoleWriteLine("Sequential transform loop: {0} secs", swElapsedTotalSeconds); ConsoleWriteLine("Main thread ending"); } } {0} secs",

The following output was produced using a dual-core processor:

Main thread starting Parallel initialization loop: 10537757 secs Sequential initialization loop: 03457628 secs Parallel transform loop: 42246675 secs Sequential transform loop: 53849959 secs Main thread ending

u + C;

Notice the following two lists are both properly constructed, although they re different from one another Both lists explain the updates currently being installed on a company s website