Search Results for "gpu processing" - Page 4
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Showing 116 open source projects for "gpu processing"
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cocoNLP
A Chinese information extraction tool
cocoNLP is a lightweight natural-language processing toolkit geared toward practical information extraction from raw text, especially for Chinese and mixed Chinese–English content. Instead of requiring a heavy pipeline, it focuses on quick wins such as extracting names, places, organizations, emails, phone numbers, and dates directly from unstructured sentences. The project blends pattern-based methods with NLP heuristics, giving developers dependable results for real-world texts like chats,... -
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AwesomeBump
Open Source graphic tool and alternative to Insane Bump
...It was made as an alternative to known gimp plugin Insane Bump or the commercial tool Crazy Bump. It is designed to generate normal, height, specular or ambient occlusion, metallic, roughness and other textures from a single image. Most of the image processing is done on GPU so the program runs very fast and all the parameters can be changed in real time. -
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Inviwo
Interactive Visualization Workshop
...Built in C++ with a modular, extensible architecture, Inviwo combines a visual editor (for creating data pipelines) with a powerful runtime engine that supports real-time rendering, interaction, and GPU-accelerated processing. It’s widely used in scientific domains for building and sharing visualizations of complex data such as medical imaging, simulations, and machine learning models. The platform supports both novice users through its graphical interface and advanced users through scripting and plugin development. -
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GPUImage 2
Framework for GPU-accelerated video and image processing
GPUImage 2 is the second generation of the GPUImage framework, an open source project for performing GPU-accelerated image and video processing on Mac, iOS, and now Linux. The original GPUImage framework was written in Objective-C and targeted Mac and iOS, but this latest version is written entirely in Swift and can also target Linux and future platforms that support Swift code. The objective of the framework is to make it as easy as possible to set up and perform realtime video processing or machine vision against image or video sources. ... -
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Tensorpack
A Neural Net Training Interface on TensorFlow, with focus on speed
...On common CNNs, it runs training 1.2~5x faster than the equivalent Keras code. Your training can probably gets faster if written with Tensorpack. Scalable data-parallel multi-GPU / distributed training strategy is off-the-shelf to use. Squeeze the best data loading performance of Python with tensorpack.dataflow. Symbolic programming (e.g. tf.data) does not offer the data processing flexibility needed in research. Tensorpack squeezes the most performance out of pure Python with various auto parallelization strategies. ... -
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PixelFlow
A Processing/Java library for high performance GPU-Computing (GLSL)
PixelFlow is a Processing library focused on advanced graphics and visual effects, offering an extensive suite of GPU-based tools for visual artists, researchers, and creative coders. It enables real-time simulation and rendering of complex effects such as fluid dynamics, reaction-diffusion systems, soft shadows, and more, all powered by GLSL shaders. Its modular structure allows for chaining and composing various visual effects easily, making it ideal for installations, performances, and visual experimentation. -
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Starling Filters
A collection of filters for use with the Starling AS3 framework
Starling-Filters is an open-source collection of filter effects for the Starling AS3 framework. These filters allow developers using Starling (a GPU accelerated 2D rendering framework in Flash/AIR) to apply image processing / visual effects (e.g. blur, glow, etc.) in their Starling-based applications. The repo has versions for Starling 2.0 (on master) and older filters archived for Starling 1.x. A collection of filters for use with the Starling AS3 framework. The master branch contains filters for use with Starling 2.0. -
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Caffe
A fast open framework for deep learning
...It’s got an expressive architecture that encourages application and innovation, and extensible code that’s great for active development. Caffe also offers great speed, capable of processing over 60M images per day with a single NVIDIA K40 GPU. It’s arguably one of the fastest convnet implementations around. Caffe is developed by the Berkeley AI Research (BAIR)/The Berkeley Vision and Learning Center (BVLC) and a great community of contributors that continue to make Caffe state-of-the-art in both code and models. ... -
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waifu2x-cpu-torch-vks
waifu2x fork without CUDA
The original is here: https://github.com/nagadomi/waifu2x Differences: - CPU only, no CUDA needed; - double-precision version; - GPU and CPU-trained models (on the fly conversion in memory); - adapted for Lua v5.2 (works with Torch 7 on x32 Ubuntu). 31-12-2016: - removed unneeded data copies, left from CUDA processing; - removed large duplicate files (see "ReadMe" before use). -
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openAviToGif
Video to GIF converter
With openAviToGif you can convert video into an animated gif file. What's new in v0.6: -GPU processing -improvements and bugfixes Use your video card to speed up the conversion. Recommended for those who have high-end video cards. -
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Scene is a computer vision framework that performs background subtraction and object tracking, using two traditional algorithms and three more recent algorithms based on neural networks and fuzzy classification rules. For each detected object, Scene sends TUIO messages to one or several client applications. The present release features GPU accelerated versions of all the background subtraction methods and morphological post processing of the object blobs with dilation and erosion filters, implemented in OpenCL. The framework was mainly designed as a toolkit for the rapid development of interactive art projects that explore dynamics of complex environments. The Scene GUI runs and compiles under Windows, Linux, and MacOS X, and is available in both 32 bit and 64 bit versions.
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ND2D
A Flash Molehill (Stage3D) GPU accelerated 2D game engine
ND2D is a 2D game framework for Flash that uses Stage3D / Molehill (i.e. the GPU acceleration in newer Flash Player versions). It allows game developers to build 2D games with lots of sprites, leveraging GPU for better performance. It includes display tree constructs, sprite sheets, particle systems, cameras, post-processing etc., made to simplify building high-performance 2D content in Flash. ND2D was built to make an ease use of hardware accelerated 2D content in the Flashplayer. ... -
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GLIP-LIB
OpenGL Image Processing LIBrary
GLIP-Lib is an OpenGL image processing library written in C++. It features all the necessary tools to quickly build texture filters and pipelines and operate them on the GPU. -
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Accelerated Feature Extraction Tool
A fast GPU accelerated feature extraction software for speech analysis
A fast feature extraction software tool for speech analysis and processing. It incorporates standard MFCC, PLP, and TRAPS features. The tool is a specially designed to process very large audio data sets. It uses GPU acceleration if compatible GPU available (CUDA as weel as OpenCL, NVIDIA, AMD, and Intel GPUs are supported). CPU SSE intrinsic instruction set is used in cases where no compatible GPU present. -
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mapgraph
Massively Parallel Graph processing on GPUs -- now part of Blazegraph
Mapgraph is SYSTAP’s disruptive new technology to exploit the main memory bandwidth advantages of GPUs. The early work was co-developed with the University of Utah SCI Institute and has its pedigree in the UINTAH software running on over 750M cores on the TITAN Super Computer. Today, SYSTAP has commercialized this technology into it’s Blazegraph Accelerator and Blazegraph HPC products. Checkout our options for GPU acceleration of graphs or contact us to learn more: ... -
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LDNI-based Solid Modeling
Solid Modeling Framework in GPU
This project provides a set of solid modeling tools which aims to help processing 3D objects with complex topology and geometry that are widely used in many industrial applications (e.g., microstructure design and manufacturing, biomedical products and applications, jewelry products, reverse engineering). The system is completely GPU-based and heavily based on an implicit representation named LDNI. -
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Ezys
Ezys 3D medical image registration program
Ezys is a non-linear 3D medical image registration program. Ezys fully exploits the parallel computing power of inexpensive commercial graphics processing units (GPU), resulting in a very fast and accurate program capable of running on desktop PCs and even some laptops. On these systems, non-linear image registrations take less than a minute to complete. Ezys implements a diffeomorphic inverse consistent image registration algorithm with a demons-style regularization based on a non-parametric free form deformation model. ... -
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gxLibrary : C++ (CUDA+AMP+CPU)
C++ library for easy simulations on any CUDA/AMP/CPU or remote PC
Helps to program ( in C++) simulations or long-running calculations with many iterations. Easily write C++ code that is same as single-threaded ( or shorter and simpler) , and gxLibrary will compile and run that code as massive multi-threaded on any available GPU (CUDA/AMP) or CPU, either on local or remote PC. Write code functions once, just like they would be written for regular single-threaded cases - no need to write separate code for CUDA or AMP or CPU, or to modify code for... -
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away3d-core-fp11
Away3D engine for Flash Player 11
Away3D Core FP11 is a real-time 3D graphics engine for Flash Player 11 that leverages Stage3D for GPU acceleration. It provides a powerful framework for rendering 3D models, animations, lighting, and materials within ActionScript 3 applications. Ideal for interactive media, games, and visualizations, Away3D simplifies working with 3D scenes in Flash and supports common file formats like OBJ and MD5. -
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HIPAcc
Heterogeneous Image Processing Acceleration (HIPACC) Framework
HIPAcc development has moved to github: https://github.com/hipacc HIPAcc allows to design image processing kernels and algorithms in a domain-specific language (DSL). From this high-level description, low-level target code for GPU accelerators is generated using source-to-source translation. As back ends, the framework supports CUDA, OpenCL, and Renderscript. HIPAcc allows programmers to develop imaging applications while providing high productivity, flexibility and portability as well as competitive performance: the same algorithm description serves as basis for targeting different GPU accelerators and low-level languages. -
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GENIE (GEne-geNe IntEraction)
GPU based Parallel Gene-Gene Interaction Analysis
...Here we present a novel software package GENIE, which utilizes the power of multiple GPU or CPU processor cores to parallelize the interaction analysis. Citation: Chikkagoudar, S., Wang, K., & Li, M. (2011). GENIE: a software package for gene-gene interaction analysis in genetic association studies using multiple GPU or CPU cores. BMC research notes, 4(1), 158. -
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gpgmpi
An GPGMP in C++/OpenCL with improved time step algorithm
The need for speed is always a challenge for large-scale stochastic simulation. This software is based on a parallel stochastic simulation algorithm already implemented on Graphics Processing Unit (GPU) for inhomogeneous reaction-drift-diffusion systems. We suggest an improved choice of time step which turns out to be almost 3 or even more times fast with nearly identical accuracy. The software is now completely implemented in C++ and OpenCL which only depends on boost library and GPU vendor’s OpenCL SDK. It can be easily set up with Microsoft Visual Studio on Windows platform. ... -
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FASTCUDA
Execution of CUDA programs on a Heterogeneous FPGA-based Architecture
FASTCUDA methodology proposes the use of CUDA, a Graphical Processing Unit (GPU) language which exposes parallelism at source code in a FPGA hardware acceleration environment.The FASTCUDA toolset splits, with minimal user intervention, application's code into two parts: one that will be compiled and executed as parallel software in an embedded Multi-core, and another that will be synthesized and implemented with multiple special-purpose accelerators in hardware. -
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Java/C Comparative Benchmarks
Java and C Comparative Performance Benchmarks
...Some of the benchmarks are also implemented in Python and Scala. There are benchmarks for bit twiddling, numerical computing, data structure manipulation, concurrent computing, callouts to native libraries, and, graphics processing units (GPU) utilization. -
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LBP in multiple platforms
LBP implementation in multiple computing platforms (ARM,GPU, DSP...)
...When selecting a suitable LBP implementation platform, the specific application and its requirements in terms of performance, size, energy efficiency, cost and developing time has to be carefully considered. This is a software toolbox that collects software implementations of the Local Binary Pattern operator in several platforms: - OpenCL for CPU & GPU - OpenCL for GPU (branchless) - C code optimized for ARM - OpenGL ES 2.0 shaders mobile GPUs - C code for TI C64x DSP core (branchless) - C code for TTA processor synthesis If you use the code somewhere, please cite: Bordallo López M., Nieto A., Boutellier J., Hannuksela J., and Silvén O. "Evaluation of real-time LBP computing in multiple architectures," Journal of Real Time Image Processing, 2014
