Since it does not make sense to stock resistors in increments of 1% if their tolerance is only guaranteed to the nearest 5%, resistor values are available in standardized sets of values in increments approximately equal to their tolerance. This percentage is called a tolerance common ones are 1% and 5%. The exact value of a resistor is guaranteed to be within a percentage of its marked value. The size of that span is amazing-it is like a kilometer-long ruler marked in millimeters! Modern multimeters typically measure from 1 Ω up to 1 MΩ (a million Ohms) or more. Measuring resistance uses the same approach as setting the range and uses the same probe jacks, but now the mode selector is set to resistance (abbreviated with the unit Ω). In the last chapter, you learned to measure voltages with a digital multimeter (DMM) by setting the mode to voltage, limiting the range to the smallest that did not cause an out-of-range error, and then placing the black probe in the COM jack and the red probe in the shared voltage/resistance jack. The way in which consideration of the number of registers required, the details of data dependency in advancing the state, and the desire for memory coalescence in storing the output lead to different implementations in the three cases is of most importance. This library is not multithreaded, but is thread safe and contains all the necessary skip-ahead functions to advance the generators' states. The Intel random number generators are contained in the vector statistical library (VSL). Although there is much in common in the underlying mathematical formulation of these three generators, there are also very significant differences owing to differences in the size of the state information required by each generator. The key to the parallelization is that each CUDA thread block generates a particular block of numbers within the original sequence, and to do this step, it needs an efficient skip-ahead algorithm to jump to the start of its block. In each case, the random number sequence that is generated is identical to that produced on a CPU by the standard sequential algorithm. This chapter discusses the parallelization of three very popular random number generators. Random number generation is a key component of many forms of simulation, and fast parallel generation is particularly important for the naturally parallel Monte Carlo simulations that are used extensively in computational finance and many areas of computational science and engineering. Paul Woodhams, in GPU Computing Gems Emerald Edition, 2011 Publisher Summary Std::random_device is a non-deterministic uniform random bit generator, although implementations are allowed to implement std::random_device using a pseudo-random number engine if there is no support for non-deterministic random number generation.Parallelization Techniques for Random Number Generators Newer "Minimum standard", recommended by Park, Miller, and Stockmeyer in 1993 ģ2-bit Mersenne Twister by Matsumoto and Nishimura, 1998 Ħ4-bit Mersenne Twister by Matsumoto and Nishimura, 2000 Ģ4-bit RANLUX generator by Martin Lüscher and Fred James, 1994 Ĥ8-bit RANLUX generator by Martin Lüscher and Fred James, 1994 Discovered in 1969 by Lewis, Goodman and Miller, adopted as "Minimal standard" in 1988 by Park and Miller
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