214 research outputs found
A GPU-enabled solver for time-constrained linear sum assignment problems
This paper deals with solving large instances of the Linear Sum Assignment Problems (LSAPs) under realtime constraints, using Graphical Processing Units (GPUs). The motivating scenario is an industrial application for P2P live streaming that is moderated by a central tracker that is periodically solving LSAP instances to optimize the connectivity of thousands of peers. However, our findings are generic enough to be applied in other contexts. Our main contribution is a parallel version of a heuristic algorithm called Deep Greedy Switching (DGS) on GPUs using the CUDA programming language. DGS sacrifices absolute optimality in favor of a substantial speedup in comparison to classical LSAP solvers like the Hungarian and auctioning methods. We show the modifications needed to parallelize the DGS algorithm and the performance gains of our approach compared to a sequential CPU-based implementation of DGS and a mixed CPU/GPU-based implementation of it
Comparison of Different Parallel Implementations of the 2+1-Dimensional KPZ Model and the 3-Dimensional KMC Model
We show that efficient simulations of the Kardar-Parisi-Zhang interface
growth in 2 + 1 dimensions and of the 3-dimensional Kinetic Monte Carlo of
thermally activated diffusion can be realized both on GPUs and modern CPUs. In
this article we present results of different implementations on GPUs using CUDA
and OpenCL and also on CPUs using OpenCL and MPI. We investigate the runtime
and scaling behavior on different architectures to find optimal solutions for
solving current simulation problems in the field of statistical physics and
materials science.Comment: 14 pages, 8 figures, to be published in a forthcoming EPJST special
issue on "Computer simulations on GPU
Simulation of 1+1 dimensional surface growth and lattices gases using GPUs
Restricted solid on solid surface growth models can be mapped onto binary
lattice gases. We show that efficient simulation algorithms can be realized on
GPUs either by CUDA or by OpenCL programming. We consider a
deposition/evaporation model following Kardar-Parisi-Zhang growth in 1+1
dimensions related to the Asymmetric Simple Exclusion Process and show that for
sizes, that fit into the shared memory of GPUs one can achieve the maximum
parallelization speedup ~ x100 for a Quadro FX 5800 graphics card with respect
to a single CPU of 2.67 GHz). This permits us to study the effect of quenched
columnar disorder, requiring extremely long simulation times. We compare the
CUDA realization with an OpenCL implementation designed for processor clusters
via MPI. A two-lane traffic model with randomized turning points is also
realized and the dynamical behavior has been investigated.Comment: 20 pages 12 figures, 1 table, to appear in Comp. Phys. Com
Activity recognition from videos with parallel hypergraph matching on GPUs
In this paper, we propose a method for activity recognition from videos based
on sparse local features and hypergraph matching. We benefit from special
properties of the temporal domain in the data to derive a sequential and fast
graph matching algorithm for GPUs.
Traditionally, graphs and hypergraphs are frequently used to recognize
complex and often non-rigid patterns in computer vision, either through graph
matching or point-set matching with graphs. Most formulations resort to the
minimization of a difficult discrete energy function mixing geometric or
structural terms with data attached terms involving appearance features.
Traditional methods solve this minimization problem approximately, for instance
with spectral techniques.
In this work, instead of solving the problem approximatively, the exact
solution for the optimal assignment is calculated in parallel on GPUs. The
graphical structure is simplified and regularized, which allows to derive an
efficient recursive minimization algorithm. The algorithm distributes
subproblems over the calculation units of a GPU, which solves them in parallel,
allowing the system to run faster than real-time on medium-end GPUs
Analysis of A Splitting Approach for the Parallel Solution of Linear Systems on GPU Cards
We discuss an approach for solving sparse or dense banded linear systems
on a Graphics Processing Unit (GPU) card. The
matrix is possibly nonsymmetric and
moderately large; i.e., . The ${\it split\ and\
parallelize}{\tt SaP}{\bf A}{\bf A}_ii=1,\ldots,P{\bf A}_i{\tt SaP::GPU}{\tt PARDISO}{\tt SuperLU}{\tt MUMPS}{\tt SaP::GPU}{\tt MKL}{\tt SaP::GPU}{\tt SaP::GPU}$ is publicly available and distributed as
open source under a permissive BSD3 license.Comment: 38 page
Real-time multitarget tracking for sensor-based sorting – A new implementation of the auction algorithm for graphics processing units
Utilizing parallel algorithms is an established way of increasing performance in systems that are bound to real-time restrictions. Sensor-based sorting is a machine vision application for which firm real-time requirements need to be respected in order to reliably remove potentially harmful entities from a material feed. Recently, employing a predictive tracking approach using multitarget tracking in order to decrease the error in the physical separation in optical sorting has been proposed. For implementations that use hard associations between measurements and tracks, a linear assignment problem has to be solved for each frame recorded by a camera. The auction algorithm can be utilized for this purpose, which also has the advantage of being well suited for parallel architectures. In this paper, an improved implementation of this algorithm for a graphics processing unit (GPU) is presented. The resulting algorithm is implemented in both an OpenCL and a CUDA based environment. By using an optimized data structure, the presented algorithm outperforms recently proposed implementations in terms of speed while retaining the quality of output of the algorithm. Furthermore, memory requirements are significantly decreased, which is important for embedded systems. Experimental results are provided for two different GPUs and six datasets. It is shown that the proposed approach is of particular interest for applications dealing with comparatively large problem sizes
Real-time people tracking in a camera network
Visual tracking is a fundamental key to the recognition and analysis of human behaviour.
In this thesis we present an approach to track several subjects using multiple
cameras in real time. The tracking framework employs a numerical Bayesian estimator,
also known as a particle lter, which has been developed for parallel implementation on
a Graphics Processing Unit (GPU). In order to integrate multiple cameras into a single
tracking unit we represent the human body by a parametric ellipsoid in a 3D world.
The elliptical boundary can be projected rapidly, several hundred times per subject per
frame, onto any image for comparison with the image data within a likelihood model.
Adding variables to encode visibility and persistence into the state vector, we tackle the
problems of distraction and short-period occlusion. However, subjects may also disappear
for longer periods due to blind spots between cameras elds of view. To recognise
a desired subject after such a long-period, we add coloured texture to the ellipsoid surface,
which is learnt and retained during the tracking process. This texture signature
improves the recall rate from 60% to 70-80% when compared to state only data association.
Compared to a standard Central Processing Unit (CPU) implementation, there
is a signi cant speed-up ratio
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