high performance computing on graphics processing units: hgpu.org

This paper describes the implementation of network coding on OpenGL-enabled graphics cards. Network coding is an interesting approach to increase the capacity and robustness in multi-hop networks. The current problem is to implement random linear network coding on mobile devices which are limited in computational power, energy, and memory. Some mobile devices are equipped with […]

This paper examines the problem of moving object detection. More precisely, it addresses the difficult scenarios where background scene textures in the video might change over time. In this paper, we formulate the problem mathematically as minimizing a constrained risk functional motivated from the large margin principle. It is a generalization of the one class […]

The rapid pace of development of graphic processor units (GPUs) in recent years in terms of performance and programmability has attracted the attention of those seeking to leverage alternative architectures for better performance than that which commodity CPUs can provide. In this paper, the potential of the GPU in automatically structuring video is examined, specifically […]

Graphics Processing Units (GPUs) have become a competitive accelerator for applications outside the graphics domain, mainly driven by the improvements in GPU programmability. Although the Compute Unified Device Architecture (CUDA) is a simple C-like interface for programming NVIDIA GPUs, porting applications to CUDA remains a challenge to average programmers. In particular, CUDA places on the […]

Graphics processing units (GPUs) with a few hundred extremely simple processors represent a paradigm shift for highly parallel computations. We use this emergent GPU architecture to provide a first demonstration of the feasibility of real time ML decoding (in software) of a high rate space-time block code that is representative of codes incorporated in 4th […]

We have implemented 20,000-word large vocabulary continuous speech recognition (LVCSR) systems employing H- and C-level weighted finite state transducer (WFST) based networks on Graphics Processing Units (GPUs). Both the emission probability computation and the Viterbi beam search are implemented on the GPU in a data-parallel manner to minimize the extra data transfer time between the […]

This paper deals with a method for interactive rendering of photorealistic images, which is a fundamental technology in the field of virtual reality. Since the latest graphics processing units (GPUs) are programmable, they are expected to be useful for various applications including numerical computation and image processing. This paper proposes a method for focus measurement […]

As the number of data points, feature dimensionality, and number of centers for clustering algorithms increase, computational tractability becomes a problem. The fuzzy c-means has a large degree of inherent algorithmic parallelism that modern CPU architectures do not exploit. Many pattern recognition algorithms can be sped up on a graphics processing unit (GPU) as long […]

In this study we test the idea of using a graphics processing unit (GPU) as an embedded co-processor for real-time detection of European Union (EU) speed-limit signs. The input to the system is a set of grayscale videos recorded from a forward-facing camera mounted in a vehicle. We introduce a new technique for implementing the […]

We design and implement Mars, a MapReduce runtime system accelerated with graphics processing units (GPUs). MapReduce is a simple and flexible parallel programming paradigm originally proposed by Google, for the ease of large-scale data processing on thousands of CPUs. Compared with CPUs, GPUs have an order of magnitude higher computation power and memory bandwidth. However, […]

The task of reconstructing an object from its projections via tomographic methods is a time-consuming process due to the vast complexity of the data. For this reason, manufacturers of equipment for computed tomography (CT), both medical and industrial, rely mostly on special ASICs to obtain the fast reconstruction times required in clinical, industrial, and security […]

Spectral mixture analysis is an important task for remotely sensed hyperspectral data interpretation. In spectral unmixing, both the determination of spectrally pure signatures (endmembers) and the unmixing process that interprets mixed pixels as combinations of endmembers are computationally expensive procedures. An exciting recent development in the field of commodity computing is the emergence of programmable […]