45,108 research outputs found
Parallel Tempering Simulation of the three-dimensional Edwards-Anderson Model with Compact Asynchronous Multispin Coding on GPU
Monte Carlo simulations of the Ising model play an important role in the
field of computational statistical physics, and they have revealed many
properties of the model over the past few decades. However, the effect of
frustration due to random disorder, in particular the possible spin glass
phase, remains a crucial but poorly understood problem. One of the obstacles in
the Monte Carlo simulation of random frustrated systems is their long
relaxation time making an efficient parallel implementation on state-of-the-art
computation platforms highly desirable. The Graphics Processing Unit (GPU) is
such a platform that provides an opportunity to significantly enhance the
computational performance and thus gain new insight into this problem. In this
paper, we present optimization and tuning approaches for the CUDA
implementation of the spin glass simulation on GPUs. We discuss the integration
of various design alternatives, such as GPU kernel construction with minimal
communication, memory tiling, and look-up tables. We present a binary data
format, Compact Asynchronous Multispin Coding (CAMSC), which provides an
additional speedup compared with the traditionally used Asynchronous
Multispin Coding (AMSC). Our overall design sustains a performance of 33.5
picoseconds per spin flip attempt for simulating the three-dimensional
Edwards-Anderson model with parallel tempering, which significantly improves
the performance over existing GPU implementations.Comment: 15 pages, 18 figure
Development of a GPU-based Monte Carlo dose calculation code for coupled electron-photon transport
Monte Carlo simulation is the most accurate method for absorbed dose
calculations in radiotherapy. Its efficiency still requires improvement for
routine clinical applications, especially for online adaptive radiotherapy. In
this paper, we report our recent development on a GPU-based Monte Carlo dose
calculation code for coupled electron-photon transport. We have implemented the
Dose Planning Method (DPM) Monte Carlo dose calculation package (Sempau et al,
Phys. Med. Biol., 45(2000)2263-2291) on GPU architecture under CUDA platform.
The implementation has been tested with respect to the original sequential DPM
code on CPU in phantoms with water-lung-water or water-bone-water slab
geometry. A 20 MeV mono-energetic electron point source or a 6 MV photon point
source is used in our validation. The results demonstrate adequate accuracy of
our GPU implementation for both electron and photon beams in radiotherapy
energy range. Speed up factors of about 5.0 ~ 6.6 times have been observed,
using an NVIDIA Tesla C1060 GPU card against a 2.27GHz Intel Xeon CPU
processor.Comment: 13 pages, 3 figures, and 1 table. Paper revised. Figures update
Accelerating Monte Carlo simulations with an NVIDIA® graphics processor
Modern graphics cards, commonly used in desktop computers, have evolved beyond a simple interface between processor and display to incorporate sophisticated calculation engines that can be applied to general purpose computing. The Monte Carlo algorithm for modelling photon transport in turbid media has been implemented on an NVIDIA® 8800gt graphics card using the CUDA toolkit. The Monte Carlo method relies on following the trajectory of millions of photons through the sample, often taking hours or days to complete. The graphics-processor implementation, processing roughly 110 million scattering events per second, was found to run more than 70 times faster than a similar, single-threaded implementation on a 2.67 GHz desktop computer
A Shuffled Complex Evolution Metropolis algorithm for optimization and uncertainty assessment of hydrologic model parameters
Markov Chain Monte Carlo (MCMC) methods have become increasingly popular for estimating the posterior probability distribution of parameters in hydrologic models. However, MCMC methods require the a priori definition of a proposal or sampling distribution, which determines the explorative capabilities and efficiency of the sampler and therefore the statistical properties of the Markov Chain and its rate of convergence. In this paper we present an MCMC sampler entitled the Shuffled Complex Evolution Metropolis algorithm (SCEM-UA), which is well suited to infer the posterior distribution of hydrologic model parameters. The SCEM-UA algorithm is a modified version of the original SCE-UA global optimization algorithm developed by Duan et al. [1992]. The SCEM-UA algorithm operates by merging the strengths of the Metropolis algorithm, controlled random search, competitive evolution, and complex shuffling in order to continuously update the proposal distribution and evolve the sampler to the posterior target distribution. Three case studies demonstrate that the adaptive capability of the SCEM-UA algorithm significantly reduces the number of model simulations needed to infer the posterior distribution of the parameters when compared with the traditional Metropolis-Hastings samplers
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