Pseudo-random Numbers in Computing
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Pseudo-random Numbers in Computing

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Questions and Answers

What is the purpose of calling the hash function twice?

  • To reduce computational time
  • To eliminate possible collisions
  • To simplify the hashing process
  • To ensure the randomness of the output (correct)

    • Which of the following is NOT listed as a use of randomness?

  • Random location
  • Random geometrical patterns
  • Random movement
  • Random noise reduction (correct)

    • How can randomness be applied to movement according to the content?

  • By varying the speed or direction of moving objects (correct)
  • By varying the shape of moving objects
  • By varying the size of moving objects
  • By varying the color of moving objects

    • In the context of randomness, what do random patterns typically involve?

    <p>Varying the contents of areas</p> Signup and view all the answers

    Which type of noise is mentioned as a more complex example of randomness?

    <p>White noise</p> Signup and view all the answers

    What differentiates random numbers generated by a computer from true randomness?

    <p>They can be repeated exactly.</p> Signup and view all the answers

    What is a drawback of using analog randomness for generating random numbers?

    <p>Results are dependent on momentary situations.</p> Signup and view all the answers

    What is the basic function of a Pseudo-random Binary Sequence (PRBS)?

    <p>To create chaotic sequences using bit manipulation.</p> Signup and view all the answers

    Why do computers need a seed value when generating pseudo-random numbers?

    <p>To generate the initial value that influences the output.</p> Signup and view all the answers

    What is the advantage of using simplex noise over gradient noise?

    <p>Simplex noise reduces blocking artifacts.</p> Signup and view all the answers

    In what way can a sine function be used to generate random numbers?

    <p>By applying transformations to its input.</p> Signup and view all the answers

    What is a limitation of standard random number libraries mentioned?

    <p>They are sequential and not suitable for shaders.</p> Signup and view all the answers

    What is the essential characteristic of computers that poses a challenge in generating random numbers?

    <p>They are designed to be deterministic.</p> Signup and view all the answers

    What method is suggested for generating random sequences directly on the GPU?

    <p>Running a random generator in a shader.</p> Signup and view all the answers

    How can random patterns contribute to procedural images?

    <p>By introducing variability to enhance realism.</p> Signup and view all the answers

    What is a drawback of feeding new random numbers every frame from the CPU?

    <p>It leads to increased CPU load.</p> Signup and view all the answers

    Which of the following is not mentioned as a technique for generating noise in GLSL?

    <p>Employing traditional noise generation algorithms.</p> Signup and view all the answers

    What is a common approach to creating noise textures?

    <p>Generating them in a controlled environment and storing in a buffer.</p> Signup and view all the answers

    Which function pairs are commonly used to work with pseudo-random numbers in standard libraries?

    <p>rand() and srand()</p> Signup and view all the answers

    What is one benefit of using noise textures in shaders?

    <p>They can be used for static elements and some animations.</p> Signup and view all the answers

    What must be considered about random number generation in the context of shader programming?

    <p>It must accommodate GPU parallel processing requirements.</p> Signup and view all the answers

    What is the primary benefit of using a random texture generated on the CPU and uploaded to a GPU?

    <p>It is GPU local and fast.</p> Signup and view all the answers

    What characteristic does repeatable randomness have when using static seeds?

    <p>It produces the same random number every time.</p> Signup and view all the answers

    How can truncated trigonometric numbers be generated according to the described method?

    <p>By using sin(x) multiplied by a large number.</p> Signup and view all the answers

    What is the purpose of using the frac() function with the sine function in randomness generation?

    <p>To produce a non-sequential random number generator.</p> Signup and view all the answers

    What is the drawback of employing truncated harmonic functions for randomness generation?

    <p>They can produce different results on different machines.</p> Signup and view all the answers

    What ensures the consistency of random numbers generated by the polynomial hash function?

    <p>The modulo operation applied to the results.</p> Signup and view all the answers

    Which of the following describes an improper method to generate random pixel values?

    <p>Taking pixel positions by π steps.</p> Signup and view all the answers

    What is the main purpose of using random intensity in pixel values?

    <p>To create visual diversity and noise-based images.</p> Signup and view all the answers

    Study Notes

    Pseudo-random Numbers

    • Computers are deterministic; thus, obtaining true randomness is impossible
    • Generate randomness using:
      • Heat + A/D conversion: Drawback - not repeatable, dependent on conditions (heat level)
      • Pseudo-random binary sequence (PRBS)
      • Shift bits and XOR with some bits
      • Requires a "seed" (start value) and a "mask" (target bits)
      • May have short sequences
    • Random library function:
      • Most run-time libraries have pseudo-random functions
      • High-quality random functions with long sequences
      • Not cryptographically secure, but sufficient for typical applications
      • Not suitable for shaders due to their sequential nature
    • GLSL random number solutions:
      • Noise texture: Pre-generated noise in a buffer; easy to use, static, and can be used for animations (e.g., snow)
      • Continuous feeding: Run a random generator on the host (CPU) and send new values every frame. It leads to high data traffic and CPU load.
      • Generate sequences on the GPU: Run in a shader; take a previous number from a buffer (texture) and generate a new one. Initiate by uploading a random texture from the CPU. Saves to a texture (FBO). Fast and suitable for changing randomness over time.
      • Repeatable randomness: Generate random numbers from static seeds; same random number every time. Suitable for noise-based images and expandable geometry.
      • Truncated trigonometric numbers: Use built-in functions. Multiply sin(x) by a large number (M), calculate the fractional part of the product, and use the result as a random number.
    • Generating random sequences on the GPU:
      • Use the previous value from a buffer (texture) to generate a new value.
      • Must initialize by uploading a random texture generated on the CPU.
      • Save the output to a texture (FBO).
      • This provides fast and local (GPU) randomness, suitable for animating noise effects over time.
    • Repeatable randomness:
      • Generate random numbers from static seeds
      • Result in the same random number every time
      • Suitable for noise-based images and expandable geometry.
    • Truncated trigonometric numbers:
      • Use built-in functions, like sin().
      • Multiply sin(x) by a large number (M), calculate the fractional part of the product, and use the result as a random number.
      • This approach provides a non-sequential random number generator that only requires the pixel position.
    • Random pixel values:
      • Generate random values to control the intensity of each pixel.
    • Permutation polynomials:
      • Ensure identical results across different machines, as they rely on integer-based functions and the modulo operation.
      • Example: hash = (34x^2 + 10x) mod 289
      • This hash function needs to be called twice for optimal randomness.
      • Provide a robust and consistent solution for generating randomness.
    • Uses of Randomness:
      • Random patterns
      • Random geometrical patterns
      • Random movement
      • Random location
      • Random geometry

    Splines

    • Splines are mathematical functions used to represent curves
    • Cubic splines are commonly used in computer graphics
    • They provide smooth and controlled transitions between points

    Noise Functions

    • White noise: Every value is random and independent
    • Coloured noise: Contains correlations and patterns
    • Perlin noise: Smooth and continuous noise; commonly used for textures
    • Voronoi noise: Generates tessellated patterns; useful for geometric patterns and textures

    Filtering Noise

    • Smooth out or enhance noise characteristics
    • Can create more natural or interesting patterns

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    Description

    Explore the concepts of pseudo-random number generation and its applications in computing. This quiz covers various methods of generating randomness, including techniques used in GLSL and limitations of standard random libraries. Test your understanding of the principles behind pseudo-randomness and its implications in programming.

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