165,760 research outputs found

### Accelerating the Fourier split operator method via graphics processing units

Current generations of graphics processing units have turned into highly
parallel devices with general computing capabilities. Thus, graphics processing
units may be utilized, for example, to solve time dependent partial
differential equations by the Fourier split operator method. In this
contribution, we demonstrate that graphics processing units are capable to
calculate fast Fourier transforms much more efficiently than traditional
central processing units. Thus, graphics processing units render efficient
implementations of the Fourier split operator method possible. Performance
gains of more than an order of magnitude as compared to implementations for
traditional central processing units are reached in the solution of the time
dependent Schr\"odinger equation and the time dependent Dirac equation

### Computational Physics on Graphics Processing Units

The use of graphics processing units for scientific computations is an
emerging strategy that can significantly speed up various different algorithms.
In this review, we discuss advances made in the field of computational physics,
focusing on classical molecular dynamics, and on quantum simulations for
electronic structure calculations using the density functional theory, wave
function techniques, and quantum field theory.Comment: Proceedings of the 11th International Conference, PARA 2012,
Helsinki, Finland, June 10-13, 201

### Accelerating NBODY6 with Graphics Processing Units

We describe the use of Graphics Processing Units (GPUs) for speeding up the
code NBODY6 which is widely used for direct $N$-body simulations. Over the
years, the $N^2$ nature of the direct force calculation has proved a barrier
for extending the particle number. Following an early introduction of force
polynomials and individual time-steps, the calculation cost was first reduced
by the introduction of a neighbour scheme. After a decade of GRAPE computers
which speeded up the force calculation further, we are now in the era of GPUs
where relatively small hardware systems are highly cost-effective. A
significant gain in efficiency is achieved by employing the GPU to obtain the
so-called regular force which typically involves some 99 percent of the
particles, while the remaining local forces are evaluated on the host. However,
the latter operation is performed up to 20 times more frequently and may still
account for a significant cost. This effort is reduced by parallel SSE/AVX
procedures where each interaction term is calculated using mainly single
precision. We also discuss further strategies connected with coordinate and
velocity prediction required by the integration scheme. This leaves hard
binaries and multiple close encounters which are treated by several
regularization methods. The present nbody6-GPU code is well balanced for
simulations in the particle range $10^4-2 \times 10^5$ for a dual GPU system
attached to a standard PC.Comment: 8 pages, 3 figures, 2 tables, MNRAS accepte

### Improved Parallel Rabin-Karp Algorithm Using Compute Unified Device Architecture

String matching algorithms are among one of the most widely used algorithms
in computer science. Traditional string matching algorithms efficiency of
underlaying string matching algorithm will greatly increase the efficiency of
any application. In recent years, Graphics processing units are emerged as
highly parallel processor. They out perform best of the central processing
units in scientific computation power. By combining recent advancement in
graphics processing units with string matching algorithms will allows to speed
up process of string matching. In this paper we proposed modified parallel
version of Rabin-Karp algorithm using graphics processing unit. Based on that,
result of CPU as well as parallel GPU implementations are compared for
evaluating effect of varying number of threads, cores, file size as well as
pattern size.Comment: Information and Communication Technology for Intelligent Systems
(ICTIS 2017

### Large-scale Ferrofluid Simulations on Graphics Processing Units

We present an approach to molecular-dynamics simulations of ferrofluids on
graphics processing units (GPUs). Our numerical scheme is based on a
GPU-oriented modification of the Barnes-Hut (BH) algorithm designed to increase
the parallelism of computations. For an ensemble consisting of one million of
ferromagnetic particles, the performance of the proposed algorithm on a Tesla
M2050 GPU demonstrated a computational-time speed-up of four order of magnitude
compared to the performance of the sequential All-Pairs (AP) algorithm on a
single-core CPU, and two order of magnitude compared to the performance of the
optimized AP algorithm on the GPU. The accuracy of the scheme is corroborated
by comparing the results of numerical simulations with theoretical predictions

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