19,975 research outputs found
Differential Evolution Markov Chain with snooker updater and fewer chains
Differential Evolution Markov Chain (DE-MC) is an adaptive MCMC algorithm, in which multiple chains are run in parallel. Standard DE-MC requires at least N=2d chains to be run in parallel, where d is the dimensionality of the posterior. This paper extends DE-MC with a snooker updater and shows by simulation and real examples that DE-MC can work for d up to 50â100 with fewer parallel chains (e.g. N=3) by exploiting information from their past by generating jumps from differences of pairs of past states. This approach extends the practical applicability of DE-MC and is shown to be about 5â26 times more efficient than the optimal Normal random walk Metropolis sampler for the 97.5% point of a variable from a 25â50 dimensional Student t 3 distribution. In a nonlinear mixed effects model example the approach outperformed a block-updater geared to the specific features of the mode
Effective Monte Carlo simulation on System-V massively parallel associative string processing architecture
We show that the latest version of massively parallel processing associative
string processing architecture (System-V) is applicable for fast Monte Carlo
simulation if an effective on-processor random number generator is implemented.
Our lagged Fibonacci generator can produce random numbers on a processor
string of 12K PE-s. The time dependent Monte Carlo algorithm of the
one-dimensional non-equilibrium kinetic Ising model performs 80 faster than the
corresponding serial algorithm on a 300 MHz UltraSparc.Comment: 8 pages, 9 color ps figures embedde
High performance FPGA implementation of the mersenne twister
Efficient generation of random and pseudorandom sequences is of great importance to a number of applications [4]. In this paper, an efficient implementation of the Mersenne Twister is presented. The proposed architecture has the smallest footprint of all published architectures to date and occupies only 330 FPGA slices. Partial pipelining and sub-expression simplification has been used to improve throughput per clock cycle. The proposed architecture is implemented on an RC1000 FPGA Development platform equipped with a Xilinx XCV2000E FPGA, and can generate 20 million 32 bit random numbers per second at a clock rate of 24.234 MHz. A through performance analysis has been performed, and it is observed that the proposed architecture clearly outperforms other existing implementations in key comparable performance metrics
Using parallel computation to improve Independent Metropolis--Hastings based estimation
In this paper, we consider the implications of the fact that parallel
raw-power can be exploited by a generic Metropolis--Hastings algorithm if the
proposed values are independent. In particular, we present improvements to the
independent Metropolis--Hastings algorithm that significantly decrease the
variance of any estimator derived from the MCMC output, for a null computing
cost since those improvements are based on a fixed number of target density
evaluations. Furthermore, the techniques developed in this paper do not
jeopardize the Markovian convergence properties of the algorithm, since they
are based on the Rao--Blackwell principles of Gelfand and Smith (1990), already
exploited in Casella and Robert (1996), Atchade and Perron (2005) and Douc and
Robert (2010). We illustrate those improvements both on a toy normal example
and on a classical probit regression model, but stress the fact that they are
applicable in any case where the independent Metropolis-Hastings is applicable.Comment: 19 pages, 8 figures, to appear in Journal of Computational and
Graphical Statistic
Efficient Monte Carlo Simulation of Biological Aging
A bit-string model of biological life-histories is parallelized, with
hundreds of millions of individuals. It gives the desired drastic decay of
survival probabilities with increasing age for 32 age intervals.Comment: PostScript file to appear in Int.J.Mod.Phys.
Multi-Architecture Monte-Carlo (MC) Simulation of Soft Coarse-Grained Polymeric Materials: SOft coarse grained Monte-carlo Acceleration (SOMA)
Multi-component polymer systems are important for the development of new
materials because of their ability to phase-separate or self-assemble into
nano-structures. The Single-Chain-in-Mean-Field (SCMF) algorithm in conjunction
with a soft, coarse-grained polymer model is an established technique to
investigate these soft-matter systems. Here we present an im- plementation of
this method: SOft coarse grained Monte-carlo Accelera- tion (SOMA). It is
suitable to simulate large system sizes with up to billions of particles, yet
versatile enough to study properties of different kinds of molecular
architectures and interactions. We achieve efficiency of the simulations
commissioning accelerators like GPUs on both workstations as well as
supercomputers. The implementa- tion remains flexible and maintainable because
of the implementation of the scientific programming language enhanced by
OpenACC pragmas for the accelerators. We present implementation details and
features of the program package, investigate the scalability of our
implementation SOMA, and discuss two applications, which cover system sizes
that are difficult to reach with other, common particle-based simulation
methods
Quantum Monte Carlo for large chemical systems: Implementing efficient strategies for petascale platforms and beyond
Various strategies to implement efficiently QMC simulations for large
chemical systems are presented. These include: i.) the introduction of an
efficient algorithm to calculate the computationally expensive Slater matrices.
This novel scheme is based on the use of the highly localized character of
atomic Gaussian basis functions (not the molecular orbitals as usually done),
ii.) the possibility of keeping the memory footprint minimal, iii.) the
important enhancement of single-core performance when efficient optimization
tools are employed, and iv.) the definition of a universal, dynamic,
fault-tolerant, and load-balanced computational framework adapted to all kinds
of computational platforms (massively parallel machines, clusters, or
distributed grids). These strategies have been implemented in the QMC=Chem code
developed at Toulouse and illustrated with numerical applications on small
peptides of increasing sizes (158, 434, 1056 and 1731 electrons). Using 10k-80k
computing cores of the Curie machine (GENCI-TGCC-CEA, France) QMC=Chem has been
shown to be capable of running at the petascale level, thus demonstrating that
for this machine a large part of the peak performance can be achieved.
Implementation of large-scale QMC simulations for future exascale platforms
with a comparable level of efficiency is expected to be feasible
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