Method for generating high-speed parallel true random numbers with ultra-high scalability

Abstract

The invention provides a method for generating high-speed parallel true random numbers with an ultra-high scalability. The method comprises the steps that firstly, spectrum cutting is conducted on an ultra-short pulse sequence generated by an optical device, so that N paths of ultra-narrow-band ultras-short pulse sequences are obtained; secondly, time domain widening is conducted, so that large-pulse-width narrow-band short pulse sequences are obtained and then converted into electrical signals; thirdly, the signals enter an analog-digital converter to be converted into corresponding high and low levels, and therefore N paths of independent true random numbers with the repetition frequency being f are generated. The high-speed parallel true random number sequence generated through the method does not have periodicity, the code rate of each path can reach the magnitude order of 10 Gbps, at least 1000 paths of independent parallel true random numbers can be output at the same time, the scalability of an existing parallel true random number generator is improved at least 3-4 magnitude orders, and the modern large-scale parallel computing and high-speed secretive network communication demands can be met to a large extend.

Description

technical field [0001] The invention relates to a parallel random number generation method, in particular to a super-expandable high-speed parallel true random number generation method, which is applied to the fields of Monte Carlo simulation, large-scale parallel computing and secure communication. Background technique [0002] Monte Carlo simulation has important applications in nuclear physics, computational chemistry, biomedicine, financial engineering, macroeconomics, meteorology, sociology and other fields. [0003] Random number is the cornerstone of Monte Carlo simulation, its quality determines the accuracy of Monte Carlo simulation, and its rate restricts the simulation speed; and the more random numbers consumed in simulation, the higher the quality requirements for random numbers. [0004] With the increasing complexity of the calculation model and the increasing amount of calculations, the current Monte Carlo simulation generally uses multiple processors to exec...

AI Technical Summary

Problems solved by technology

[0011] The purpose of the present invention is to provide a high-speed parallel true random number generation method with super scalability to solve the problems of insufficient code rate and poor scalability in the prior art

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