Parallel Simulation in Metropolis
نویسنده
چکیده
Electronic system design has been becoming more and more complex. It is very common to have dozens of IP modules in a single system. To quickly simulate the system is becoming a challenge. On the other hand, computing facilities, on which people run actual simulation, are becoming more and more powerful by having more powerful single processor and by building parallel machines. However, there is not too much attempts in exploring parallelism in electronic design automation community esp. for system level design. In this paper, I present a parallel simulator targeting Symmetric Multi-Processor (SMP) machines for Metropolis design environment. Since the careful implementation of parallelism, the portability and scalability are maximized.
منابع مشابه
Wilson Loop Calculations in Four - Dimensional Latfice Gauge Theory on The
Pure SU(4) gauge theory is simulated by Monte Carlo methods on an 8~lattice. The method of Metropolis et al. is used to equilibriate the space—time lattice. All Wilson loops up to size 4 x 4 are calculated. Because of memory requirements we work on the 2 Mword CDC CYBER 205 at Colorado State University and take full advantage of the parallel processing capabilities of this vector machine.
متن کاملJeffrey Hyman’s Summer Research at UCLA
We examine three Monte Carlo sampling methods on idealized examples. Implementing the Metropolis algorithm on created functions, we compare the single temperature to four temperatures running in parallel. Using simulated annealing and parallel tempering on the traveling salesman problem, we examine what each of the methods is capable of exploring. We focus specifically on parallel tempering’s a...
متن کاملMarkov Chain Monte Carlo and Related Topics
This article provides a brief review of recent developments in Markov chain Monte Carlo methodology. The methods discussed include the standard Metropolis-Hastings algorithm, the Gibbs sampler, and various special cases of interest to practitioners. It also devotes a section on strategies for improving mixing rate of MCMC samplers, e.g., simulated tempering, parallel tempering, parameter expans...
متن کاملExploring Hybrid Monte Carlo in Bayesian Computation
Hybrid Monte Carlo (HMC) has been successfully applied to molecular simulation problems since its introduction in the late 1980s. Its use in Bayesian computation, however, is relatively recent and rare (Neal 1996). In this article, we investigate statistical models in which HMC shows an edge over the more standard Monte Carlo techniques such as the Metropolis algorithm and the Gibbs sampler. Th...
متن کاملA Bootstrap Metropolis-Hastings Algorithm for Bayesian Analysis of Big Data
Markov chain Monte Carlo (MCMC) methods have proven to be a very powerful tool for analyzing data of complex structures. However, their computer-intensive nature, which typically require a large number of iterations and a complete scan of the full dataset for each iteration, precludes their use for big data analysis. In this paper, we propose the so-called bootstrap Metropolis-Hastings (BMH) al...
متن کامل