Mar, 15

Efficient Shadows for GPU-based Volume Raycasting

GPU-based raycasting has emerged as the defacto standard for interactive volume rendering on off-the-shelf graphics hardware. Even though in theory this technique can be easily extended by shadow feelers in order to support shadows, this obvious approach has a major impact on the rendering performance. In this paper we will investigate shadowing extensions for GPU- […]
Mar, 15

Interactive GPU-based Collision Detection

F two closed polygonal objects with outfacing normals intersect each other there exist one or more lines that intersect these objects at at least two consecutive front or back facing object points. In this work we present a method to efficiently detect these lines using depth-peeling and simple fragment operations. Of all polygons only those […]
Mar, 15

GPUGI: Global Illumination Effects on the GPU

In this tutorial we explain how global illumination rendering methods can be implemented on Shader Model 3.0 GPUs. These algorithms do not follow the conventional local illumination model of DirectX/OpenGL pipelines, but require global geometric or illumination information when shading a point. In addition to the theory and state of the art of these approaches, […]
Mar, 15

Compensated Visual Hull with GPU-Based Optimization

We propose an advanced visual hull technique to compensate for outliers using the reliabilities of silhouettes. The proposed method consists of a foreground extraction technique with multiple thresholds based on the Generalized Gaussian Family model and a compensated visual hull algorithm. We proved that the proposed technique constructs a compact visual hull even in the […]
Mar, 15

A framework for efficient and scalable execution of domain-specific templates on GPUs

Graphics processing units (GPUs) have emerged as important players in the transition of the computing industry from sequential to multi- and many-core computing. We propose a software framework for execution of domain-specific parallel templates on GPUs, which simultaneously raises the abstraction level of GPU programming and ensures efficient execution with forward scalability to large data […]
Mar, 15

A Package for OpenCL Based Heterogeneous Computing on Clusters with Many GPU Devices

Heterogeneous systems provide new opportunities to increase the performance of parallel applications on clusters with CPU and GPU architectures. Currently, applications that utilize GPU devices run their device-executable code on local devices in their respective hosting-nodes. This paper presents a package for running OpenMP, C++ and unmodified OpenCL applications on clusters with many GPU devices. […]
Mar, 15

Efficient Canny Edge Detection Using a GPU

Recent GPUs, which have many processing units connected with a global memory, can be used for general purpose parallel computation. Users can develop parallel programs running on GPUs using programming architecture called CUDA (Compute Unified Device Architecture). The main contribution of this paper is to implement a Canny edge detection algorithm on CUDA. The experimental […]
Mar, 15

Dense point trajectories by GPU-accelerated large displacement optical flow

Dense and accurate motion tracking is an important requirement for many video feature extraction algorithms. In this paper we provide a method for computing point trajectories based on a fast parallel implementation of a recent optical flow algorithm that tolerates fast motion. The parallel implementation of large displacement optical flow runs about 78x faster than […]
Mar, 15

Accelerating Nearest Neighbor Search on Manycore Systems

We develop methods for accelerating metric similarity search that are effective on modern hardware. Our algorithms factor into easily parallelizable components, making them simple to deploy and efficient on multicore CPUs and GPUs. Despite the simple structure of our algorithms, their search performance is provably sublinear in the size of the database, with a factor […]
Mar, 15

Fast Sparse Matrix-Vector Multiplication on GPUs: Implications for Graph Mining

Scaling up the sparse matrix-vector multiplication kernel on modern Graphics Processing Units (GPU) has been at the heart of numerous studies in both academia and industry. In this article we present a novel non-parametric, self-tunable, approach to data representation for computing this kernel, particularly targeting sparse matrices representing power-law graphs. Using real data, we show […]
Mar, 14

Fast Human Detection with Cascaded Ensembles

Detecting people in images is a challenging task because of the variability in clothing and illumination conditions, and the wide range of poses that people can adopt. To discriminate the human shape clearly, Dalal and Triggs [1] proposed a gradient based, robust feature set that yielded excellent detection results. This method computes locally normalized gradient […]
Mar, 14

Fast Human Detection with Cascaded Ensembles on the GPU

We investigate a fast pedestrian localization framework that integrates the cascade-of-rejectors approach with the Histograms of Oriented Gradients (HoG) features on a data parallel architecture. The salient features of humans are captured by HoG blocks of variable sizes and locations which are chosen by the AdaBoost algorithm from a large set of possible blocks. We […]
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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
  • GPU device 0: AMD/ATI Radeon HD 5870 2GB, 850MHz
  • 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

Completed OpenCL project should be uploaded via User dashboard (see instructions and example there), compilation and execution terminal output logs will be provided to the user.

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