Computer Science

Download An Introduction to Parallel Programming by Peter Pacheco PDF

By Peter Pacheco

Writer Peter Pacheco makes use of an academic method of convey scholars tips to boost powerful parallel courses with MPI, Pthreads, and OpenMP. the 1st undergraduate textual content to without delay handle compiling and working parallel courses at the new multi-core and cluster structure, An advent to Parallel Programming explains the best way to layout, debug, and assessment the functionality of disbursed and shared-memory courses. undemanding workouts train scholars find out how to collect, run and adjust instance programs.

Key features:

  • Takes an academic procedure, beginning with small programming examples and construction steadily to more difficult examples
  • Focuses on designing, debugging and comparing the functionality of disbursed and shared-memory programs
  • Explains the right way to increase parallel courses utilizing MPI, Pthreads, and OpenMP programming models
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    Extra info for An Introduction to Parallel Programming

    Sample text

    3 Vector Addition Before we continue looking at the PVector class and its add() method (purely for the sake of learning since it’s already implemented for us in Processing itself), let’s examine vector addition using the notation found in math and physics textbooks. Vectors are typically written either in boldface type or with an arrow on top. For the purposes of this book, to distinguish a vector from a scalar (scalar refers to a single value, such as an integer or a floating point number), we’ll use the arrow notation: → • Vector: u • Scalar: x Let’s say I have the following two vectors: 33 Chapter 1.

    If R2 is not less than P, go back to step 1 and start over. Here we are saying that the likelihood that a random value will qualify is equal to the random number itself. 1 for R1. This means that R1 will have a 10% chance of qualifying. 83 for R1 then it will have a 83% chance of qualifying. The higher the number, the greater the likelihood that we will actually use it. Here is a function (named for the Monte Carlo method, which was named for the Monte Carlo casino) that implements the above algorithm, returning a random value between 0 and 1.

    One technique is to fill an array with a selection of numbers—some of which are repeated—then choose random numbers from that array and generate events based on those choices. int[] stuff = new int[5] stuff[0] = 1; stuff[1] = 1; 1 is stored in the array twice, making it more likely to be picked. length)); Picking a random element from an array Running this code will produce a 40% chance of printing the value 1, a 20% chance of printing 2, and a 40% chance of printing 3. We can also ask for a random number (let’s make it simple and just consider random floating point values between 0 and 1) and allow an event to occur only if our random number is within a certain range.

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