1,312 research outputs found
Simulation of reaction-diffusion processes in three dimensions using CUDA
Numerical solution of reaction-diffusion equations in three dimensions is one
of the most challenging applied mathematical problems. Since these simulations
are very time consuming, any ideas and strategies aiming at the reduction of
CPU time are important topics of research. A general and robust idea is the
parallelization of source codes/programs. Recently, the technological
development of graphics hardware created a possibility to use desktop video
cards to solve numerically intensive problems. We present a powerful parallel
computing framework to solve reaction-diffusion equations numerically using the
Graphics Processing Units (GPUs) with CUDA. Four different reaction-diffusion
problems, (i) diffusion of chemically inert compound, (ii) Turing pattern
formation, (iii) phase separation in the wake of a moving diffusion front and
(iv) air pollution dispersion were solved, and additionally both the Shared
method and the Moving Tiles method were tested. Our results show that parallel
implementation achieves typical acceleration values in the order of 5-40 times
compared to CPU using a single-threaded implementation on a 2.8 GHz desktop
computer.Comment: 8 figures, 5 table
Thread-Scalable Evaluation of Multi-Jet Observables
A leading-order, leading-color parton-level event generator is developed for
use on a multi-threaded GPU. Speed-up factors between 150 and 300 are obtained
compared to an unoptimized CPU-based implementation of the event generator. In
this first paper we study the feasibility of a GPU-based event generator with
an emphasis on the constraints imposed by the hardware. Some studies of Monte
Carlo convergence and accuracy are presented for PP -> 2,...,10 jet observables
using of the order of 1e11 events.Comment: 16 pages, 5 figures, 3 table
Air pollution modelling using a graphics processing unit with CUDA
The Graphics Processing Unit (GPU) is a powerful tool for parallel computing.
In the past years the performance and capabilities of GPUs have increased, and
the Compute Unified Device Architecture (CUDA) - a parallel computing
architecture - has been developed by NVIDIA to utilize this performance in
general purpose computations. Here we show for the first time a possible
application of GPU for environmental studies serving as a basement for decision
making strategies. A stochastic Lagrangian particle model has been developed on
CUDA to estimate the transport and the transformation of the radionuclides from
a single point source during an accidental release. Our results show that
parallel implementation achieves typical acceleration values in the order of
80-120 times compared to CPU using a single-threaded implementation on a 2.33
GHz desktop computer. Only very small differences have been found between the
results obtained from GPU and CPU simulations, which are comparable with the
effect of stochastic transport phenomena in atmosphere. The relatively high
speedup with no additional costs to maintain this parallel architecture could
result in a wide usage of GPU for diversified environmental applications in the
near future.Comment: 5 figure
Parallel Algorithm for Solving Kepler's Equation on Graphics Processing Units: Application to Analysis of Doppler Exoplanet Searches
[Abridged] We present the results of a highly parallel Kepler equation solver
using the Graphics Processing Unit (GPU) on a commercial nVidia GeForce 280GTX
and the "Compute Unified Device Architecture" programming environment. We apply
this to evaluate a goodness-of-fit statistic (e.g., chi^2) for Doppler
observations of stars potentially harboring multiple planetary companions
(assuming negligible planet-planet interactions). We tested multiple
implementations using single precision, double precision, pairs of single
precision, and mixed precision arithmetic. We find that the vast majority of
computations can be performed using single precision arithmetic, with selective
use of compensated summation for increased precision. However, standard single
precision is not adequate for calculating the mean anomaly from the time of
observation and orbital period when evaluating the goodness-of-fit for real
planetary systems and observational data sets. Using all double precision, our
GPU code outperforms a similar code using a modern CPU by a factor of over 60.
Using mixed-precision, our GPU code provides a speed-up factor of over 600,
when evaluating N_sys > 1024 models planetary systems each containing N_pl = 4
planets and assuming N_obs = 256 observations of each system. We conclude that
modern GPUs also offer a powerful tool for repeatedly evaluating Kepler's
equation and a goodness-of-fit statistic for orbital models when presented with
a large parameter space.Comment: 19 pages, to appear in New Astronom
Fat vs. thin threading approach on GPUs: application to stochastic simulation of chemical reactions
We explore two different threading approaches on a graphics processing unit (GPU) exploiting two different characteristics of the current GPU architecture. The fat thread approach tries to minimise data access time by relying on shared memory and registers potentially sacrificing parallelism. The thin thread approach maximises parallelism and tries to hide access latencies. We apply these two approaches to the parallel stochastic simulation of chemical reaction systems using the stochastic simulation algorithm (SSA) by Gillespie (J. Phys. Chem, Vol. 81, p. 2340-2361, 1977). In these cases, the proposed thin thread approach shows comparable performance while eliminating the limitation of the reaction system’s size
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