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On credibility of simulation studies of telecommunication networks
 IEEE Communications Magazine
, 2002
"... In telecommunication networks, as in many other areas of science and engineering, proliferation of computers as research tools has resulted in the adoption of computer simulation as the most commonly used paradigm of scientific investigations. This, together with a plethora of existing simulation la ..."
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In telecommunication networks, as in many other areas of science and engineering, proliferation of computers as research tools has resulted in the adoption of computer simulation as the most commonly used paradigm of scientific investigations. This, together with a plethora of existing simulation languages and packages, has created a popular opinion that simulation is mainly an exercise in computer programming. In new computing environments, programming can be minimised, or even fully replaced, by the manipulation of icons (representing prebuilt programming objects containing basic functional blocks of simulated systems) on a computer monitor. One can say that we have witnessed another success of modern science and technology: the emergence of wonderful and powerful tools for exploring and predicting the behaviour of such complex, stochastic dynamic systems as telecommunication networks. But this enthusiasm is not shared by all researchers in this area. An opinion is spreading that one cannot rely on the majority of the published results on performance evaluation studies of telecommunication networks based on stochastic simulation, since they lack credibility. Indeed, the spread of this phenomenon is so wide that one can speak about a deep crisis of credibility. In this paper, this claim is supported by the results of a survey of over 2200 publications on telecommunication
A fast high quality pseudo random number generator for graphics processing units
 In IEEE Congress on Evolutionary Computation, 2008. CEC 2008
, 2008
"... Anyone who considers arithmetical methods of producing random digits is, of course, in a state of sin. John von Neumann Abstract—Limited numerical precision of nVidia GeForce 8800 GTX and other GPUs requires careful implementation of PRNGs. The ParkMiller PRNG is programmed using G80’s native Value ..."
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Cited by 14 (4 self)
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Anyone who considers arithmetical methods of producing random digits is, of course, in a state of sin. John von Neumann Abstract—Limited numerical precision of nVidia GeForce 8800 GTX and other GPUs requires careful implementation of PRNGs. The ParkMiller PRNG is programmed using G80’s native Value4f floating point in RapidMind C++. Speed up is more than 40. Code is available via ftp cs.ucl.ac.uk genetic/gpcode/randomnumbers/gpu parkmiller.tar.gz I.
Linear Congruential Generators for Parallel MonteCarlo: the LeapFrog Case.
 Monte Carlo Methods and Applications
, 1997
"... In this paper we consider parallel streams of pseudorandom numbers (PRNs) which are obtained by splitting linear congruential generators (LCGs) using the leapfrog technique. We employ the spectral test to compute an a priori figure of merit which rates the amount of correlation that is present in s ..."
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In this paper we consider parallel streams of pseudorandom numbers (PRNs) which are obtained by splitting linear congruential generators (LCGs) using the leapfrog technique. We employ the spectral test to compute an a priori figure of merit which rates the amount of correlation that is present in such sequences for given step size and dimension. It is shown that for some widely used LCGs there exist practically relevant splitting parameters such that the according parallel streams have poor quality. As can be seen from a sample MonteCarlo integration study, these theoretical findings have high practical importance. 1 Introduction Parallel computations in the field of stochastic simulation (e.g. [14, 9]) require a source of pseudorandom numbers (PRNs) which can be distributed among the single processing units. This is most efficiently achieved by assigning a generator to each such processing unit [15]. In order to be able to Research supported by the Austrian Science Foundation (FW...
Parallel Streams of Linear Random Numbers in the Spectral Test
 in High Dimensions, Monte Carlo Methods and Applications
, 1998
"... ing with credit is permitted. To copy otherwise, to republish, to post on servers, to redistribute to lists, or to use any component of this work in other works, requires prior specific permission and/or a fee. Permissions may be requested from Publications Dept, ACM Inc., 1515 Broadway, New York, N ..."
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Cited by 5 (4 self)
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ing with credit is permitted. To copy otherwise, to republish, to post on servers, to redistribute to lists, or to use any component of this work in other works, requires prior specific permission and/or a fee. Permissions may be requested from Publications Dept, ACM Inc., 1515 Broadway, New York, NY 10036 USA fax +1 (212) 8690481, or permissions@acm.org. Parallel Streams of Linear Random Numbers in the Spectral Test Karl Entacher Austrian Science Fund (FWF projects no. P11143MAT and P12441MAT) This paper reports analyses of subsequences of linear congruential pseudorandom numbers by means of the spectral test. Such subsequences occur in particular simulation setups or as methods to obtain parallel streams of pseudorandom numbers for parallel and distributed simulation. Especially in the latter case, two kinds of substreams are of special interest: lagged random numbers with step sizes k, and consecutive streams of random numbers of length l. We show how to analyze correlations ...
PRNG Random Numbers on GPU
, 2007
"... Anyone who considers arithmetical methods of producing random digits is, of course, in a state of sin. John von Neumann Limited numerical precision of nVidia GeForce 8800 GTX and other GPUs requires careful implementation of PRNGs. The ParkMiller PRNG is programmed using G80’s native Value4f floati ..."
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Anyone who considers arithmetical methods of producing random digits is, of course, in a state of sin. John von Neumann Limited numerical precision of nVidia GeForce 8800 GTX and other GPUs requires careful implementation of PRNGs. The ParkMiller PRNG is programmed using G80’s native Value4f floating point in RapidMind C++. Speed up is more than 40. Code is available via ftp
Security evaluation of email encryption using random noise generated by LCGs. 15 th CCSC:CS
, 2005
"... Theoretically, using any Linear Congruence Generator (LCG) to generate pseudorandom numbers for cryptographic purposes is problematic because of its predictableness. On the other hand, due to its simplicity and efficiency, we think that the LCG should not be completely ignored. Since the random num ..."
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Theoretically, using any Linear Congruence Generator (LCG) to generate pseudorandom numbers for cryptographic purposes is problematic because of its predictableness. On the other hand, due to its simplicity and efficiency, we think that the LCG should not be completely ignored. Since the random numbers generated by the LCG are predictable, it is clear that we cannot use them directly. However, we shall not introduce too much complication in the implementation which will compromise the reasons, simplicity and efficiency, of choosing the LCG. Thus, we propose an easy encryption method using an LCG for email encryption. To see how practical in predicting random numbers produced by an LCG, we implement Plumstead’s inference algorithm [2] and run it on some numbers generated by the easiest congruence: Xn+1 = aXn+ b mod m. Based on the result, we confirm the theoretical fault of the LCG, that is, simply increasing the size of the modulus does not significantly increase the difficulty of breaking the sequence. Our remedy is to break a whole random number into pieces and use them separately (with interference from another source, in our case, English text). We use 16bytes random numbers and embed each byte of the random number as noise in one text character. In such a way, we can avoid revealing enough numbers for the attacker to predict.
EFFICIENT LATTICE ASSESSMENT for LCG . . .
, 2001
"... In the present paper we show how to speed up lattice parameter searches for Monte Carlo and quasi–Monte Carlo node sets. The classical measure for such parameter searches is the spectral test which is based on a calculation of the shortest nonzero vector in a lattice. Instead of the shortest vecto ..."
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In the present paper we show how to speed up lattice parameter searches for Monte Carlo and quasi–Monte Carlo node sets. The classical measure for such parameter searches is the spectral test which is based on a calculation of the shortest nonzero vector in a lattice. Instead of the shortest vector we apply an approximation given by the LLL algorithm for lattice basis reduction. We empirically demonstrate the speedup and the quality loss obtained by the LLL reduction, and we present important applications for parameter selections.
MAKING SIMJAVA COUNT
, 2002
"... SimJava is a discreteevent processbased simulation API. Being easy to use and flexible, it has found widespread use among simulation practitioners either as a simulation tool in itself or as the basis for other tools and extensions. However, SimJava’s simplicity is also its major shortcoming, requ ..."
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SimJava is a discreteevent processbased simulation API. Being easy to use and flexible, it has found widespread use among simulation practitioners either as a simulation tool in itself or as the basis for other tools and extensions. However, SimJava’s simplicity is also its major shortcoming, requiring the modeller to manually undertake a number of tedious and errorprone tasks. This project’s aim is to enhance SimJava in several ways in order to provide a powerful simulation tool, free of such burdens. The sampling methods used will be improved, sophisticated statistical support will be provided, powerful transient and termination conditions will be made available, and finally, detailed graphical output analysis will be provided as an option for simulations. These enhancements will be made available in an easy to use and automated manner, providing the modeller with powerful functionality and allowing him to focus on the modelling aspects of experiments. Acknowledgements I would like to take this opportunity to thank my supervisor, Jane Hillston, for her invaluable support throughout the course of this project. Being always available and eager to provide
Using Linear Congruential Generators for Cryptographic Purposes
"... We try to provide an alternative attitude toward the use of a Linear Congruential Generator (LCG here after) in generating pseudorandom numbers for some cryptographic purpose. In particular, we choose email encryption as our cryptographic application. Our encryption will be considered secure if the ..."
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We try to provide an alternative attitude toward the use of a Linear Congruential Generator (LCG here after) in generating pseudorandom numbers for some cryptographic purpose. In particular, we choose email encryption as our cryptographic application. Our encryption will be considered secure if the attacker cannot infer the pseudorandom numbers without knowing the parameters of the LCG. We implement Plumstead’s inference algorithm [2] for an unknown LCG and our experimental results show that simply increasing the size of the modulus of the LCG does not significantly increase the difficulty of breaking the system. The only way to circumvent the weakness of the LCG, as we conclude, is to hide the generated numbers from the attacker. We suggest a practical attack on the method proposed in [11] and then introduce a much stronger version to patch the loophole without compromising the simplicity of the LCG. Moreover, we speculate that our new version of using the LCG in email encryption may resist the known plaintext attack and, therefore, there is no need to distribute a new set of parameters for the LCG for each encryption.
Abstract Good random number generators are (not so) easy to find
"... Every random number generator has its advantages and deficiencies. There are no ``safe' ' generators. The practitioner's problem is how to decide which random number generator will suit his needs best. In this paper, we will discuss criteria for good random number generators: theoreti ..."
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Every random number generator has its advantages and deficiencies. There are no ``safe' ' generators. The practitioner's problem is how to decide which random number generator will suit his needs best. In this paper, we will discuss criteria for good random number generators: theoretical support, empirical evidence and practical aspects. We will study several recent algorithms that perform better than most generators in actual use. We will compare the different methods and supply numerical results as well as selected pointers and links to important literature and other sources. Additional information on random number generation, including the code of most algorithms discussed in this paper is available from our webserver under the