1,334 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
Neural 3D Mesh Renderer
For modeling the 3D world behind 2D images, which 3D representation is most
appropriate? A polygon mesh is a promising candidate for its compactness and
geometric properties. However, it is not straightforward to model a polygon
mesh from 2D images using neural networks because the conversion from a mesh to
an image, or rendering, involves a discrete operation called rasterization,
which prevents back-propagation. Therefore, in this work, we propose an
approximate gradient for rasterization that enables the integration of
rendering into neural networks. Using this renderer, we perform single-image 3D
mesh reconstruction with silhouette image supervision and our system
outperforms the existing voxel-based approach. Additionally, we perform
gradient-based 3D mesh editing operations, such as 2D-to-3D style transfer and
3D DeepDream, with 2D supervision for the first time. These applications
demonstrate the potential of the integration of a mesh renderer into neural
networks and the effectiveness of our proposed renderer
From Big Data to Big Displays: High-Performance Visualization at Blue Brain
Blue Brain has pushed high-performance visualization (HPV) to complement its
HPC strategy since its inception in 2007. In 2011, this strategy has been
accelerated to develop innovative visualization solutions through increased
funding and strategic partnerships with other research institutions.
We present the key elements of this HPV ecosystem, which integrates C++
visualization applications with novel collaborative display systems. We
motivate how our strategy of transforming visualization engines into services
enables a variety of use cases, not only for the integration with high-fidelity
displays, but also to build service oriented architectures, to link into web
applications and to provide remote services to Python applications.Comment: ISC 2017 Visualization at Scale worksho
DDSL: Deep Differentiable Simplex Layer for Learning Geometric Signals
We present a Deep Differentiable Simplex Layer (DDSL) for neural networks for
geometric deep learning. The DDSL is a differentiable layer compatible with
deep neural networks for bridging simplex mesh-based geometry representations
(point clouds, line mesh, triangular mesh, tetrahedral mesh) with raster images
(e.g., 2D/3D grids). The DDSL uses Non-Uniform Fourier Transform (NUFT) to
perform differentiable, efficient, anti-aliased rasterization of simplex-based
signals. We present a complete theoretical framework for the process as well as
an efficient backpropagation algorithm. Compared to previous differentiable
renderers and rasterizers, the DDSL generalizes to arbitrary simplex degrees
and dimensions. In particular, we explore its applications to 2D shapes and
illustrate two applications of this method: (1) mesh editing and optimization
guided by neural network outputs, and (2) using DDSL for a differentiable
rasterization loss to facilitate end-to-end training of polygon generators. We
are able to validate the effectiveness of gradient-based shape optimization
with the example of airfoil optimization, and using the differentiable
rasterization loss to facilitate end-to-end training, we surpass state of the
art for polygonal image segmentation given ground-truth bounding boxes
Decoupled Sampling for Real-Time Graphics Pipelines
We propose decoupled sampling, an approach that decouples shading from visibility sampling in order to enable motion blur and depth-of-field at reduced cost. More generally, it enables extensions of modern real-time graphics pipelines that provide controllable shading rates to trade off quality for performance. It can be thought of as a generalization of GPU-style multisample antialiasing (MSAA) to support unpredictable shading rates, with arbitrary mappings from visibility to shading samples as introduced by motion blur, depth-of-field, and adaptive shading. It is inspired by the Reyes architecture in offline rendering, but targets real-time pipelines by driving shading from visibility samples as in GPUs, and removes the need for micropolygon dicing or rasterization. Decoupled Sampling works by defining a many-to-one hash from visibility to shading samples, and using a buffer to memoize shading samples and exploit reuse across visibility samples. We present extensions of two modern GPU pipelines to support decoupled sampling: a GPU-style sort-last fragment architecture, and a Larrabee-style sort-middle pipeline. We study the architectural implications and derive end-to-end performance estimates on real applications through an instrumented functional simulator. We demonstrate high-quality motion blur and depth-of-field, as well as variable and adaptive shading rates
Decoupled Sampling for Graphics Pipelines
We propose a generalized approach to decoupling shading from visibility sampling in graphics pipelines, which we call decoupled sampling. Decoupled sampling enables stochastic supersampling of motion and defocus blur at reduced shading cost, as well as controllable or adaptive shading rates which trade off shading quality for performance. It can be thought of as a generalization of multisample antialiasing (MSAA) to support complex and dynamic mappings from visibility to shading samples, as introduced by motion and defocus blur and adaptive shading. It works by defining a many-to-one hash from visibility to shading samples, and using a buffer to memoize shading samples and exploit reuse across visibility samples. Decoupled sampling is inspired by the Reyes rendering architecture, but like traditional graphics pipelines, it shades fragments rather than micropolygon vertices, decoupling shading from the geometry sampling rate. Also unlike Reyes, decoupled sampling only shades fragments after precise computation of visibility, reducing overshading.
We present extensions of two modern graphics pipelines to support decoupled sampling: a GPU-style sort-last fragment architecture, and a Larrabee-style sort-middle pipeline. We study the architectural implications of decoupled sampling and blur, and derive end-to-end performance estimates on real applications through an instrumented functional simulator. We demonstrate high-quality motion and defocus blur, as well as variable and adaptive shading rates
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