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The Binder Cumulant at the KosterlitzThouless Transition, 2008
 P08003 [arXiv:0804.1880] 43 Ballesteros H G, Fernandez L A, MartinMayor V, and MunozSudupe A, Finite size effects on measures of critical exponents in d=3 O(N) models
, 1996
"... We study the behaviour of the Binder cumulant on finite square lattices at the KosterlitzThouless phase transition. We determine the fixed point value of the Binder cumulant and the coefficient of the leading logarithmic correction. These calculations are supplemented with Monte Carlo simulations o ..."
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We study the behaviour of the Binder cumulant on finite square lattices at the KosterlitzThouless phase transition. We determine the fixed point value of the Binder cumulant and the coefficient of the leading logarithmic correction. These calculations are supplemented with Monte Carlo simulations of the classical XY (plane rotator) model, the Villain model and the dual of the absolute value solidonsolid model. Using the single cluster algorithm, we simulate lattices up to L = 4096. For the lattice sizes reached, subleading corrections are needed to fit the data for the Binder cumulant. We demonstrate that the combined analysis of the Binder cumulant and the second moment correlation length over the lattice size allows for an accurate determination of the KosterlitzThouless transition temperature on relatively small lattices. We test the new method at the example of the 2component φ 4 model on the lattice.
Multiobjective Evolution based Dynamic Job Scheduler in Grid
"... Abstract—Grid computing is a high performance computing environment to fulfill largescale computational demands. It can integrate computational as well as storage resources from different networks and geographically dispersed organizations into a high performance computational & storage platfor ..."
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Abstract—Grid computing is a high performance computing environment to fulfill largescale computational demands. It can integrate computational as well as storage resources from different networks and geographically dispersed organizations into a high performance computational & storage platform. It is used to solve complex computationalintensive problems, and also provide solution to storageintensive applications with connected storage resources. Scheduling of user jobs properly on the heterogeneous resources is an important task in a grid computing environment. The main goal of scheduling is to maximize resource utilization, minimize waiting time of jobs, reduce energy consumption, minimize cost to the user after satisfying constraints of jobs and resources. We can trade off between the required level of quality of service, the deadline and the budget of user. In this paper, we propose a Multiobjective Evolutionbased Dynamic Scheduler in Grid. Our scheduler have used Multiobjective optimization technique using Genetic algorithm with pareto front approach to find efficient schedules. It explores the search space vividly to avoid stagnation and generate near optimal solution. We propose that our scheduler provides a better grip on most features of grid from perspective of grid owner as well as user. Dynamic grid environment has forced us to make it a real time dynamic scheduler. A job grouping technique is proposed for grouping finegrained jobs and for ease of computation. Experimentation on different data sets and on various parameters revealed effectiveness of multiobjective scheduling criteria and extraction of performance from grid resource. KeywordsMultiobjective, Job scheduling, GA, Grid computing, Pareto, Job grouping
Multiobjective Evolution based Dynamic Job Scheduler in Grid
, 2013
"... Grid computing is a high performance computing environment to fulfill largescale computational demands. It can integrate computational as well as storage resources from different networks and geographically dispersed organizations into a high performance computational & storage platform. It is ..."
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Grid computing is a high performance computing environment to fulfill largescale computational demands. It can integrate computational as well as storage resources from different networks and geographically dispersed organizations into a high performance computational & storage platform. It is used to solve complex computationalintensive problems, and also provide solution to storageintensive applications with connected storage resources. Scheduling of user jobs properly on the heterogeneous resources is an important task in a grid computing environment. The main goal of scheduling is to maximize resource utilization, minimize waiting time of jobs, reduce energy consumption, minimize cost to the user after satisfying constraints of jobs and resources. We can trade off between the required level of quality of service, the deadline and the budget of user.In this thesis, we propose a Multiobjective Evolutionbased Dynamic Scheduler in Grid. Our scheduler have used Multiobjective optimization technique using Genetic algorithm with pareto front approach to find efficient schedules. Our avantgarde crossover, mutation and selection operators offer exploration of search space vividly to avoid stagnation and generate near optimal solution. We propose that our scheduler provides a better grip on most features of grid from perspective of grid owner as well as user. Dynamic grid environment has forced us to make it a real time dynamic scheduler. A job grouping technique is proposed for grouping finegrained jobs and for ease of computation. Experimentation on different data sets and on various parameters revealed effectiveness of multiobjective scheduling criteria and extraction of performance from grid resource. ii Dedicated to my parents, my sister & a special friend iii
On the Fast Generation of Longperiod Pseudorandom Number Sequences
"... AbstractMonte Carlo simulations and other scientific applications that depend on random numbers are increasingly implemented in parallel configurations in programmable hardware. Highquality pseudorandom number generators (PRNGs), such as the Mersenne Twister, are based on binary linear recurrenc ..."
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AbstractMonte Carlo simulations and other scientific applications that depend on random numbers are increasingly implemented in parallel configurations in programmable hardware. Highquality pseudorandom number generators (PRNGs), such as the Mersenne Twister, are based on binary linear recurrence equations. They have extremely long periods (more than 2 1024 numbers generated before the entire sequence repeats) and wellproven statistical properties. Many software implementations of such 'longperiod' PRNGs exist, but hardware implementations are rare. We develop optimized, resourceefficient parallel architectures for longperiod PRNGs that generate multiple independent streams by exploiting the underlying algorithm as well as hardwarespecific architectural features. We demonstrate the utility of the framework through parallelized implementations of three types of PRNGs on a fieldprogrammable gate array (FPGA). The area/throughput performance is impressive: for example, compared clockforclock with a previous FPGA implementation, a "twoparallelized" 32bit Mersenne Twister uses 41% fewer resources. It can also scale to 350 MHz for a throughput of 22.4 Gbps, which is 5.5x faster than the previous implementation and 7.1x faster than a dedicated software implementation. The quality of generated random numbers is verified with standard statistical test batteries. To complete testing, we present a realworld application study by coupling our parallel hardware RNGs to the Ziggurat algorithm for generating normal random variables. The availability of fast longperiod random number generators accelerates hardwarebased scientific simulations and allows them to scale to greater complexities.
The Inefficacy of Chauvenet's Criterion for Elimination of Data Points
"... Chauvenet's criterion is commonly used for rejection of outliers from sample datasets in engineering and physical science research. Measurement and uncertainty textbooks provide conflicting information on how the criterion should be applied and generally do not refer to the original work. This ..."
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Chauvenet's criterion is commonly used for rejection of outliers from sample datasets in engineering and physical science research. Measurement and uncertainty textbooks provide conflicting information on how the criterion should be applied and generally do not refer to the original work. This study was undertaken to evaluate the efficacy of Chauvenet's criterion for improving the estimate of the standard deviation of a sample, evaluate the various interpretations on how it is to be applied, and evaluate the impact of removing detected outliers. Monte Carlo simulations using normally distributed random numbers were performed with sample sizes of 5100,000. The results show that discarding outliers based on Chauvenet's criterion is more likely to have a negative effect on estimates of mean and standard deviation than to have a positive effect. At best, the probability of improving the estimates is around 50%, which only occurs for large sample sizes.
thermodynamic Casimir force in the
, 907
"... The specific heat, the energy density and the ..."
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A Hardware Framework for the Fast Generation of Multiple Longperiod Random Number Streams
"... Stochastic simulations and other scientific applications that depend on random numbers are increasingly implemented in a parallelized manner in programmable logic. Highquality pseudorandom number generators (PRNG), such as the Mersenne Twister, are often based on binary linear recurrences and have ..."
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Stochastic simulations and other scientific applications that depend on random numbers are increasingly implemented in a parallelized manner in programmable logic. Highquality pseudorandom number generators (PRNG), such as the Mersenne Twister, are often based on binary linear recurrences and have extremely long periods (more than 2 1024). Many software implementations of such PRNGs exist, but hardware implementations are rare. We have developed an optimized, resourceefficient parallel framework for this class of random number generators that exploits the underlying algorithm as well as FPGAspecific architectural features. The framework also incorporates fast “jumpahead ” capability for these PRNGs, allowing simultaneous, independent substreams to be generated in parallel by partitioning one longperiod pseudorandom sequence. We demonstrate parallelized implementations of three types of PRNGs – the 32, 64 and 128bit SIMD Mersenne Twister – on Xilinx VirtexII Pro FPGAs. Their area/throughput performance is impressive: for example, compared clockforclock with a previous
A Monte Carlo study of threedimensional
, 811
"... KosterlitzThouless transition in thin films: ..."
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Mersenne Twister – A Pseudo Random Number Generator and its Variants
"... Random number generators(RNG) are widely being used in number of applications, particularly simulation and cryptography. They are a critical part of many cryptographic systems such as key generation, initialization vectors, message padding, nonces and many more. This paper discusses about the Mersen ..."
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Random number generators(RNG) are widely being used in number of applications, particularly simulation and cryptography. They are a critical part of many cryptographic systems such as key generation, initialization vectors, message padding, nonces and many more. This paper discusses about the Mersenne Twister(MT), a pseudo random number generator(PRNG) and its variants. It mainly emphasizes on two of its variants. SIMDOriented Fast Mersenne Twister(SFMT) which is a 128bit PRNG analogous to MT making full use of its features. And the cryptographically secure CryptMT, considered to be one of the fastest stream ciphers on a CPU with SIMD operations. It also briefly discusses the theories and the choice of parameters used in the algorithms. The requirements for a PRNG to be certified as a good and cryptographically secure PRNG will be presented. Random number generators are devices that generate a series of numbers or some kind of symbols that appear random. RNG’S are used for a variety of purposes such as simulating, modeling complex phenomena, cryptography and of course ever popular for games and gambling. There are two main approaches to generating random numbers, Pseudo Random Number Generators(PRNG) and True