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Integer igen, Integer iseed[], NagError *fail)
"... nag_rngs_compd_poisson (g05mec) generates a vector of pseudo-random integers, each from a discrete Poisson distribution with differing parameter . ..."
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nag_rngs_compd_poisson (g05mec) generates a vector of pseudo-random integers, each from a discrete Poisson distribution with differing parameter .
NAG C Library Function Document nag_rngs_binomial (g05mjc)
"... nag_rngs_binomial (g05mjc) generates a vector of pseudo-random integers from the discrete binomial distribution with parameters m and p. ..."
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nag_rngs_binomial (g05mjc) generates a vector of pseudo-random integers from the discrete binomial distribution with parameters m and p.
NAG C Library Function Document nag_rngs_poisson (g05mkc)
"... nag_rngs_poisson (g05mkc) generates a vector of pseudo-random integers from the discrete Poisson distribution with mean . ..."
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nag_rngs_poisson (g05mkc) generates a vector of pseudo-random integers from the discrete Poisson distribution with mean .
NAG C Library Function Document nag_rngs_logarithmic (g05mdc)
"... nag_rngs_logarithmic (g05mdc) generates a vector of pseudo-random integers from the discrete logarithmic distribution with parameter a. ..."
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nag_rngs_logarithmic (g05mdc) generates a vector of pseudo-random integers from the discrete logarithmic distribution with parameter a.
Integer iseed[], double r[], NagError *fail)
"... nag_rngs_geom (g05mbc) generates a vector of pseudo-random integers from the discrete geometric distribution with probability p of success at a trial. ..."
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nag_rngs_geom (g05mbc) generates a vector of pseudo-random integers from the discrete geometric distribution with probability p of success at a trial.
INTEGER FUNCTION G05EYF(R, NR) INTEGER
"... Note: before using this routine, please read the Users ’ Note for your implementation to check the interpretation of bold italicised terms and other implementation-dependent details. 1 Purpose G05EYF returns a pseudo-random integer taken from a discrete distribution defined by a reference vector R. ..."
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Note: before using this routine, please read the Users ’ Note for your implementation to check the interpretation of bold italicised terms and other implementation-dependent details. 1 Purpose G05EYF returns a pseudo-random integer taken from a discrete distribution defined by a reference vector R.
NAG C Library Function Document nag_rngs_gen_discrete (g05mzc)
"... nag_rngs_gen_discrete (g05mzc) generates a vector of pseudo-random integers from a discrete distribution with a given PDF (probability density function) or CDF (cumulative distribution function) p. ..."
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nag_rngs_gen_discrete (g05mzc) generates a vector of pseudo-random integers from a discrete distribution with a given PDF (probability density function) or CDF (cumulative distribution function) p.
NAG C Library Function Document nag_rngs_neg_bin (g05mcc)
"... nag_rngs_neg_bin (g05mcc) generates a vector of pseudo-random integers from the discrete negative binomial distribution with parameter m and probability p of success at a trial. ..."
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nag_rngs_neg_bin (g05mcc) generates a vector of pseudo-random integers from the discrete negative binomial distribution with parameter m and probability p of success at a trial.
Pseudo-Randomly Interleaved Memory
- IN PROCEEDINGS OF THE 18TH ANNUAL INTERNATIONAL SYMPOSIUM ON COMPUTER ARCHITECTURE
, 1991
"... Interleaved memories are often used to provide the high bandwidth needed by multi- processors and high performance uniprocessors. The manner in which memory locations are distributed across the memory modules has a significant influence on whether, and for which types of reference patterns, the full ..."
Abstract
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Cited by 91 (0 self)
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performance with strides that are odd integers, it can degrade badly in the face of even strides, especially strides that are a power of two. This happens because all the memory references are concentrated on a subset of the memory modules. Pseudo-
Pseudo-Random Functions and Factoring
- Proc. 32nd ACM Symp. on Theory of Computing
, 2000
"... The computational hardness of factoring integers is the most established assumption on which cryptographic primitives are based. This work presents an efficient construction of pseudorandom functions whose security is based on the intractability of factoring. In particular, we are able to constru ..."
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Cited by 18 (3 self)
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The computational hardness of factoring integers is the most established assumption on which cryptographic primitives are based. This work presents an efficient construction of pseudorandom functions whose security is based on the intractability of factoring. In particular, we are able
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158
