Tag: Monte Carlo
As is discussed here for the case of studying classical spin models of statistical mechanics by Monte Carlo simulations, only an explicit tailoring of the involved algorithms to the specific architecture under consideration allows to harvest the computational power of GPU systems.
The purpose of this work is to report on our implementation of a simple MapReduce method for performing fault-tolerant Monte Carlo computations in a massively-parallel cloud computing environment.
In this paper we present an implementation of the Monte Carlo radiosity algorithm on the GPU using CUDA. Our proposal is based on the partition of the scene into sub-scenes to be processed in parallel to exploit the graphics card structure
The answers to data assimilation questions can be expressed as path integrals over all possible state and parameter histories. We show how these path integrals can be evaluated numerically using a Markov Chain Monte Carlo method designed to run in parallel on a graphics processing unit
A new hybrid imaging-treatment modality, the MRI-Linac, involves the irradiation of the patient in the presence of a strong magnetic field. This field acts on the charged particles, responsible for depositing dose, through the Lorentz force.
Authors discuss the advantages of parallelization by multithreading on graphics processing units (GPUs) for parallel tempering Monte Carlo computer simulations of an exemplified bead-spring model for homopolymers.
Xcelerit announced the world’s fastest execution of a Monte-Carlo option pricing algorithm (Black-Scholes model) on a single unit rack-mounted system. The benchmark was carried out on a new compact 1U Supermicro 6016GT-TF-FM209 GPU SuperServer equipped with two brand-new NVIDIA Tesla M2090 GPUs driven to the max using Xcelerit’s software development kit.
In this work, it is shown how such simulations can be accelerated with the use of NVIDIA graphics processing units (GPUs) using the CUDA programming architecture. We have developed two different algorithms for lattice spin models, the first useful for equilibrium properties near a second-order phase transition point and the second for dynamical slowing down near a glass transition.
Graphics-processing units (GPUs) suitable for general-purpose numerical computation are now available with performances in excess of 1 Teraflops, faster by one to two orders of magnitude than conventional desktop CPUs. Monte Carlo particle transport algorithms are ideally suited to parallel processing architectures and so are good candidates for acceleration using a GPU. We have developed a general-purpose code that computes the transport of high energy (>1 keV) photons through arbitrary 3-dimensional geometry models, simulates their physical interactions and performs tallying and variance reduction. We describe a new algorithm, the particle-per-block technique, that provides a good match with the underlying GPU multiprocessor hardware design. Benchmarking against an existing CPU-based simulation running on a single-core of a commodity desktop CPU demonstrates that our code can accurately model X-ray transport, with an approximately 35-fold speed-up factor.
Annual Reports in Computational Chemistry provides timely and critical reviews of important topics in the broad field of computational chemistry. Topics covered include quantum chemistry, molecularmechanics, force fields, chemical education, and applications in academic and industrial settings. Series is published by Elsevier and supported by the ACS COPM division. This year’s Report (Vol. 6) highlights advances of GPUs in computational chemistry and molecular modeling.