Feb, 7

Fitting multi-planet transit models to photometric time-data series by evolution strategies

In this paper we present the application of an evolution strategy to the problem of detecting multi-planet transit events in photometric time-data series. Planetary transits occur when a planet regularly eclipses its host star, reducing stellar luminosity. The transit method is amongst the most successful detection methods for exoplanet and is presently performed by space […]
Feb, 7

Learning to Detect Roads in High-Resolution Aerial Images

Reliably extracting information from aerial imagery is a difficult problem with many practical applications. One specific case of this problem is the task of automatically detecting roads. This task is a difficult vision problem because of occlusions, shadows, and a wide variety of non-road objects. Despite 30 years of work on automatic road detection, no […]
Feb, 7

Axel: a heterogeneous cluster with FPGAs and GPUs

This paper describes a heterogeneous computer cluster called Axel. Axel contains a collection of nodes; each node can include multiple types of accelerators such as FPGAs (Field Programmable Gate Arrays) and GPUs (Graphics Processing Units). A Map-Reduce framework for the Axel cluster is presented which exploits spatial and temporal locality through different types of processing […]
Feb, 7

High-Quality Point-Based Rendering on Modern GPUs

In the last years, point-based rendering has been shown to offer the potential to outperform traditional triangle based rendering both in speed and visual quality when it comes to processing highly complex models. Existing surface splatting techniques achieve superior visual quality by proper filtering but they are still limited in rendering speed. On the other […]
Feb, 7

A New Data Layout For Set Intersection on GPUs

Set intersection is the core in a variety of problems, e.g. frequent itemset mining and sparse boolean matrix multiplication. It is well-known that large speed gains can, for some computational problems, be obtained by using a graphics processing unit (GPU) as a massively parallel computing device. However, GPUs require highly regular control flow and memory […]
Feb, 7

Real-time Spatiotemporal Stereo Matching Using the Dual-Cross-Bilateral Grid

We introduce a real-time stereo matching technique based on a reformulation of Yoon and Kweon’s adaptive support weights algorithm [1]. Our implementation uses the bilateral grid to achieve a speedup of 200x compared to a straightforward full-kernel GPU implementation, making it the fastest technique on the Middlebury website. We introduce a colour component into our […]
Feb, 7

Rendering Forest Scenes in Real-Time

Forests are crucial for scene realism in applications such as flight simulators. This paper proposes a new representation allowing for the real-time rendering of realistic forests covering an arbitrary terrain. It lets us produce dense forests corresponding to continuous non-repetitive fields made of thousands of trees with full parallax. Our representation draws on volumetric textures […]
Feb, 7

Interactive collision detection for complex and deformable models using programmable graphics hardware

In this paper we present an interactive collision detection algorithm for complex and deformable objects. For two target models, our approach rapidly calculates their region of interests (ROI), which is the overlapping of their axis aligned bounding boxes (AABBs), in CPU. The surfaces of both models inside the ROI are then voxelized using a novel […]
Feb, 7

Optimal automatic multi-pass shader partitioning by dynamic programming

Complex shaders must be partitioned into multiple passes to execute on GPUs with limited hardware resources. Automatic partitioning gives rise to an NP-hard scheduling problem that can be solved by any number of established techniques. One such technique, Dynamic Programming (DP), is commonly used for instruction scheduling and register allocation in the code generation phase […]
Feb, 6

Automatically translating a general purpose C++ image processing library for GPUs

This paper presents work-in-progress towards a C++ source-to-source translator that automatically seeks parallelizable code fragments and replaces them with code for a graphics co-processor. We report on our experience with accelerating an industrial image processing library. To increase the effectiveness of our approach, we exploit some domain-specific knowledge of the library’s semantics. We outline the […]
Feb, 6

Explicit Control of Vector Field Based Shape Deformations

Vector field based shape deformations (VFSD) have been introduced as an efficient method to deform shapes in a volume-preserving foldover-free manner. However, mainly simple implicitly defined shapes like spheres or cylinders have been explored as deformation tools by now. In contrast, boundary constraint modeling approaches enable the user to exactly define the support of the […]
Feb, 6

Lighting Details Preserving Photon Density Estimation

Standard density estimation approaches suffer from visible bias due to low-pass filtering of the lighting function. Therefore, most photon density estimation methods have been used primarily with inefficient Monte Carlo final gathering to achieve high-quality results for the indirect illumination. We present a density estimation technique for efficiently computing all-frequency global illumination in diffuse and […]
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Free GPU computing nodes at hgpu.org

Registered users can now run their OpenCL application at hgpu.org. We provide 1 minute of computer time per each run on two nodes with two AMD and one nVidia graphics processing units, correspondingly. There are no restrictions on the number of starts.

The platforms are

Node 1
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  • GPU device 1: AMD/ATI Radeon HD 6970 2GB, 880MHz
  • CPU: AMD Phenom II X6 @ 2.8GHz 1055T
  • RAM: 12GB
  • OS: OpenSUSE 13.1
  • SDK: AMD APP SDK 2.9
Node 2
  • GPU device 0: AMD/ATI Radeon HD 7970 3GB, 1000MHz
  • GPU device 1: nVidia GeForce GTX 560 Ti 2GB, 822MHz
  • CPU: Intel Core i7-2600 @ 3.4GHz
  • RAM: 16GB
  • OS: OpenSUSE 12.2
  • SDK: nVidia CUDA Toolkit 6.0.1, AMD APP SDK 2.9

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