6,490 research outputs found
GPU Accelerated Particle Visualization with Splotch
Splotch is a rendering algorithm for exploration and visual discovery in
particle-based datasets coming from astronomical observations or numerical
simulations. The strengths of the approach are production of high quality
imagery and support for very large-scale datasets through an effective mix of
the OpenMP and MPI parallel programming paradigms. This article reports our
experiences in re-designing Splotch for exploiting emerging HPC architectures
nowadays increasingly populated with GPUs. A performance model is introduced
for data transfers, computations and memory access, to guide our re-factoring
of Splotch. A number of parallelization issues are discussed, in particular
relating to race conditions and workload balancing, towards achieving optimal
performances. Our implementation was accomplished by using the CUDA programming
paradigm. Our strategy is founded on novel schemes achieving optimized data
organisation and classification of particles. We deploy a reference simulation
to present performance results on acceleration gains and scalability. We
finally outline our vision for future work developments including possibilities
for further optimisations and exploitation of emerging technologies.Comment: 25 pages, 9 figures. Astronomy and Computing (2014
Quicksilver: Fast Predictive Image Registration - a Deep Learning Approach
This paper introduces Quicksilver, a fast deformable image registration
method. Quicksilver registration for image-pairs works by patch-wise prediction
of a deformation model based directly on image appearance. A deep
encoder-decoder network is used as the prediction model. While the prediction
strategy is general, we focus on predictions for the Large Deformation
Diffeomorphic Metric Mapping (LDDMM) model. Specifically, we predict the
momentum-parameterization of LDDMM, which facilitates a patch-wise prediction
strategy while maintaining the theoretical properties of LDDMM, such as
guaranteed diffeomorphic mappings for sufficiently strong regularization. We
also provide a probabilistic version of our prediction network which can be
sampled during the testing time to calculate uncertainties in the predicted
deformations. Finally, we introduce a new correction network which greatly
increases the prediction accuracy of an already existing prediction network. We
show experimental results for uni-modal atlas-to-image as well as uni- / multi-
modal image-to-image registrations. These experiments demonstrate that our
method accurately predicts registrations obtained by numerical optimization, is
very fast, achieves state-of-the-art registration results on four standard
validation datasets, and can jointly learn an image similarity measure.
Quicksilver is freely available as an open-source software.Comment: Add new discussion
Multi-GPU Graph Analytics
We present a single-node, multi-GPU programmable graph processing library
that allows programmers to easily extend single-GPU graph algorithms to achieve
scalable performance on large graphs with billions of edges. Directly using the
single-GPU implementations, our design only requires programmers to specify a
few algorithm-dependent concerns, hiding most multi-GPU related implementation
details. We analyze the theoretical and practical limits to scalability in the
context of varying graph primitives and datasets. We describe several
optimizations, such as direction optimizing traversal, and a just-enough memory
allocation scheme, for better performance and smaller memory consumption.
Compared to previous work, we achieve best-of-class performance across
operations and datasets, including excellent strong and weak scalability on
most primitives as we increase the number of GPUs in the system.Comment: 12 pages. Final version submitted to IPDPS 201
Efficient Neural Network Implementations on Parallel Embedded Platforms Applied to Real-Time Torque-Vectoring Optimization Using Predictions for Multi-Motor Electric Vehicles
The combination of machine learning and heterogeneous embedded platforms enables new potential for developing sophisticated control concepts which are applicable to the field of vehicle dynamics and ADAS. This interdisciplinary work provides enabler solutions -ultimately implementing fast predictions using neural networks (NNs) on field programmable gate arrays (FPGAs) and graphical processing units (GPUs)- while applying them to a challenging application: Torque Vectoring on a multi-electric-motor vehicle for enhanced vehicle dynamics. The foundation motivating this work is provided by discussing multiple domains of the technological context as well as the constraints related to the automotive field, which contrast with the attractiveness of exploiting the capabilities of new embedded platforms to apply advanced control algorithms for complex control problems. In this particular case we target enhanced vehicle dynamics on a multi-motor electric vehicle benefiting from the greater degrees of freedom and controllability offered by such powertrains. Considering the constraints of the application and the implications of the selected multivariable optimization challenge, we propose a NN to provide batch predictions for real-time optimization. This leads to the major contribution of this work: efficient NN implementations on two intrinsically parallel embedded platforms, a GPU and a FPGA, following an analysis of theoretical and practical implications of their different operating paradigms, in order to efficiently harness their computing potential while gaining insight into their peculiarities. The achieved results exceed the expectations and additionally provide a representative illustration of the strengths and weaknesses of each kind of platform. Consequently, having shown the applicability of the proposed solutions, this work contributes valuable enablers also for further developments following similar fundamental principles.Some of the results presented in this work are related to activities within the 3Ccar project, which has
received funding from ECSEL Joint Undertaking under grant agreement No. 662192. This Joint Undertaking
received support from the European Union’s Horizon 2020 research and innovation programme and Germany,
Austria, Czech Republic, Romania, Belgium, United Kingdom, France, Netherlands, Latvia, Finland, Spain, Italy,
Lithuania. This work was also partly supported by the project ENABLES3, which received funding from ECSEL
Joint Undertaking under grant agreement No. 692455-2
A Genetic Programming Approach to Designing Convolutional Neural Network Architectures
The convolutional neural network (CNN), which is one of the deep learning
models, has seen much success in a variety of computer vision tasks. However,
designing CNN architectures still requires expert knowledge and a lot of trial
and error. In this paper, we attempt to automatically construct CNN
architectures for an image classification task based on Cartesian genetic
programming (CGP). In our method, we adopt highly functional modules, such as
convolutional blocks and tensor concatenation, as the node functions in CGP.
The CNN structure and connectivity represented by the CGP encoding method are
optimized to maximize the validation accuracy. To evaluate the proposed method,
we constructed a CNN architecture for the image classification task with the
CIFAR-10 dataset. The experimental result shows that the proposed method can be
used to automatically find the competitive CNN architecture compared with
state-of-the-art models.Comment: This is the revised version of the GECCO 2017 paper. The code of our
method is available at https://github.com/sg-nm/cgp-cn
Opt: A Domain Specific Language for Non-linear Least Squares Optimization in Graphics and Imaging
Many graphics and vision problems can be expressed as non-linear least
squares optimizations of objective functions over visual data, such as images
and meshes. The mathematical descriptions of these functions are extremely
concise, but their implementation in real code is tedious, especially when
optimized for real-time performance on modern GPUs in interactive applications.
In this work, we propose a new language, Opt (available under
http://optlang.org), for writing these objective functions over image- or
graph-structured unknowns concisely and at a high level. Our compiler
automatically transforms these specifications into state-of-the-art GPU solvers
based on Gauss-Newton or Levenberg-Marquardt methods. Opt can generate
different variations of the solver, so users can easily explore tradeoffs in
numerical precision, matrix-free methods, and solver approaches. In our
results, we implement a variety of real-world graphics and vision applications.
Their energy functions are expressible in tens of lines of code, and produce
highly-optimized GPU solver implementations. These solver have performance
competitive with the best published hand-tuned, application-specific GPU
solvers, and orders of magnitude beyond a general-purpose auto-generated
solver
Distributed Training Large-Scale Deep Architectures
Scale of data and scale of computation infrastructures together enable the
current deep learning renaissance. However, training large-scale deep
architectures demands both algorithmic improvement and careful system
configuration. In this paper, we focus on employing the system approach to
speed up large-scale training. Via lessons learned from our routine
benchmarking effort, we first identify bottlenecks and overheads that hinter
data parallelism. We then devise guidelines that help practitioners to
configure an effective system and fine-tune parameters to achieve desired
speedup. Specifically, we develop a procedure for setting minibatch size and
choosing computation algorithms. We also derive lemmas for determining the
quantity of key components such as the number of GPUs and parameter servers.
Experiments and examples show that these guidelines help effectively speed up
large-scale deep learning training
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