382 research outputs found
Acceleration of stereo-matching on multi-core CPU and GPU
This paper presents an accelerated version of a
dense stereo-correspondence algorithm for two different parallelism
enabled architectures, multi-core CPU and GPU. The
algorithm is part of the vision system developed for a binocular
robot-head in the context of the CloPeMa 1 research project.
This research project focuses on the conception of a new clothes
folding robot with real-time and high resolution requirements
for the vision system. The performance analysis shows that
the parallelised stereo-matching algorithm has been significantly
accelerated, maintaining 12x and 176x speed-up respectively
for multi-core CPU and GPU, compared with non-SIMD singlethread
CPU. To analyse the origin of the speed-up and gain
deeper understanding about the choice of the optimal hardware,
the algorithm was broken into key sub-tasks and the performance
was tested for four different hardware architectures
Acceleration of stereo-matching on multi-core CPU and GPU
This paper presents an accelerated version of a
dense stereo-correspondence algorithm for two different parallelism
enabled architectures, multi-core CPU and GPU. The
algorithm is part of the vision system developed for a binocular
robot-head in the context of the CloPeMa 1 research project.
This research project focuses on the conception of a new clothes
folding robot with real-time and high resolution requirements
for the vision system. The performance analysis shows that
the parallelised stereo-matching algorithm has been significantly
accelerated, maintaining 12x and 176x speed-up respectively
for multi-core CPU and GPU, compared with non-SIMD singlethread
CPU. To analyse the origin of the speed-up and gain
deeper understanding about the choice of the optimal hardware,
the algorithm was broken into key sub-tasks and the performance
was tested for four different hardware architectures
The EU Center of Excellence for Exascale in Solid Earth (ChEESE): Implementation, results, and roadmap for the second phase
publishedVersio
GPU Computing for Cognitive Robotics
This thesis presents the first investigation of the impact of GPU
computing on cognitive robotics by providing a series of novel experiments in
the area of action and language acquisition in humanoid robots and computer
vision. Cognitive robotics is concerned with endowing robots with high-level
cognitive capabilities to enable the achievement of complex goals in complex
environments. Reaching the ultimate goal of developing cognitive robots will
require tremendous amounts of computational power, which was until
recently provided mostly by standard CPU processors. CPU cores are
optimised for serial code execution at the expense of parallel execution, which
renders them relatively inefficient when it comes to high-performance
computing applications. The ever-increasing market demand for
high-performance, real-time 3D graphics has evolved the GPU into a highly
parallel, multithreaded, many-core processor extraordinary computational
power and very high memory bandwidth. These vast computational resources
of modern GPUs can now be used by the most of the cognitive robotics models
as they tend to be inherently parallel. Various interesting and insightful
cognitive models were developed and addressed important scientific questions
concerning action-language acquisition and computer vision. While they have
provided us with important scientific insights, their complexity and
application has not improved much over the last years. The experimental
tasks as well as the scale of these models are often minimised to avoid
excessive training times that grow exponentially with the number of neurons
and the training data. This impedes further progress and development of
complex neurocontrollers that would be able to take the cognitive robotics
research a step closer to reaching the ultimate goal of creating intelligent
machines. This thesis presents several cases where the application of the GPU
computing on cognitive robotics algorithms resulted in the development of
large-scale neurocontrollers of previously unseen complexity enabling the
conducting of the novel experiments described herein.European Commission Seventh Framework
Programm
NiftySim: A GPU-based nonlinear finite element package for simulation of soft tissue biomechanics
Purpose
NiftySim, an open-source finite element toolkit, has been designed to allow incorporation of high-performance soft tissue simulation capabilities into biomedical applications. The toolkit provides the option of execution on fast graphics processing unit (GPU) hardware, numerous constitutive models and solid-element options, membrane and shell elements, and contact modelling facilities, in a simple to use library.
Methods
The toolkit is founded on the total Lagrangian explicit dynamics (TLEDs) algorithm, which has been shown to be efficient and accurate for simulation of soft tissues. The base code is written in C ++++ , and GPU execution is achieved using the nVidia CUDA framework. In most cases, interaction with the underlying solvers can be achieved through a single Simulator class, which may be embedded directly in third-party applications such as, surgical guidance systems. Advanced capabilities such as contact modelling and nonlinear constitutive models are also provided, as are more experimental technologies like reduced order modelling. A consistent description of the underlying solution algorithm, its implementation with a focus on GPU execution, and examples of the toolkit’s usage in biomedical applications are provided.
Results
Efficient mapping of the TLED algorithm to parallel hardware results in very high computational performance, far exceeding that available in commercial packages.
Conclusion
The NiftySim toolkit provides high-performance soft tissue simulation capabilities using GPU technology for biomechanical simulation research applications in medical image computing, surgical simulation, and surgical guidance applications
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