4,395 research outputs found

    GPU-driven recombination and transformation of YCoCg-R video samples

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    Common programmable Graphics Processing Units (GPU) are capable of more than just rendering real-time effects for games. They can also be used for image processing and the acceleration of video decoding. This paper describes an extended implementation of the H.264/AVC YCoCg-R to RGB color space transformation on the GPU. Both the color space transformation and recombination of the color samples from a nontrivial data layout are performed by the GPU. Using mid- to high-range GPUs, this extended implementation offers a significant gain in processing speed compared to an existing basic GPU version and an optimized CPU implementation. An ATI X1900 GPU was capable of processing more than 73 high-resolution 1080p YCoCg-R frames per second, which is over twice the speed of the CPU-only transformation using a Pentium D 820

    GPU-based Image Analysis on Mobile Devices

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    With the rapid advances in mobile technology many mobile devices are capable of capturing high quality images and video with their embedded camera. This paper investigates techniques for real-time processing of the resulting images, particularly on-device utilizing a graphical processing unit. Issues and limitations of image processing on mobile devices are discussed, and the performance of graphical processing units on a range of devices measured through a programmable shader implementation of Canny edge detection.Comment: Proceedings of Image and Vision Computing New Zealand 201

    Fast Calculation of the Lomb-Scargle Periodogram Using Graphics Processing Units

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    I introduce a new code for fast calculation of the Lomb-Scargle periodogram, that leverages the computing power of graphics processing units (GPUs). After establishing a background to the newly emergent field of GPU computing, I discuss the code design and narrate key parts of its source. Benchmarking calculations indicate no significant differences in accuracy compared to an equivalent CPU-based code. However, the differences in performance are pronounced; running on a low-end GPU, the code can match 8 CPU cores, and on a high-end GPU it is faster by a factor approaching thirty. Applications of the code include analysis of long photometric time series obtained by ongoing satellite missions and upcoming ground-based monitoring facilities; and Monte-Carlo simulation of periodogram statistical properties.Comment: Accepted by ApJ. Accompanying program source (updated since acceptance) can be downloaded from http://www.astro.wisc.edu/~townsend/resource/download/code/culsp.tar.g

    Hardware-accelerated interactive data visualization for neuroscience in Python.

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    Large datasets are becoming more and more common in science, particularly in neuroscience where experimental techniques are rapidly evolving. Obtaining interpretable results from raw data can sometimes be done automatically; however, there are numerous situations where there is a need, at all processing stages, to visualize the data in an interactive way. This enables the scientist to gain intuition, discover unexpected patterns, and find guidance about subsequent analysis steps. Existing visualization tools mostly focus on static publication-quality figures and do not support interactive visualization of large datasets. While working on Python software for visualization of neurophysiological data, we developed techniques to leverage the computational power of modern graphics cards for high-performance interactive data visualization. We were able to achieve very high performance despite the interpreted and dynamic nature of Python, by using state-of-the-art, fast libraries such as NumPy, PyOpenGL, and PyTables. We present applications of these methods to visualization of neurophysiological data. We believe our tools will be useful in a broad range of domains, in neuroscience and beyond, where there is an increasing need for scalable and fast interactive visualization
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