Search Results for "cuda gpu" - Page 5
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Showing 163 open source projects for "cuda gpu"
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Deep Exemplar-based Video Colorization
The source code of CVPR 2019 paper "Deep Exemplar-based Colorization"
The source code of CVPR 2019 paper "Deep Exemplar-based Video Colorization". End-to-end network for exemplar-based video colorization. The main challenge is to achieve temporal consistency while remaining faithful to the reference style. To address this issue, we introduce a recurrent framework that unifies the semantic correspondence and color propagation steps. Both steps allow a provided reference image to guide the colorization of every frame, thus reducing accumulated propagation... -
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CUDAnative.jl
Julia support for native CUDA programming
The programming support for NVIDIA GPUs in Julia is provided by the CUDA.jl package. It is built on the CUDA toolkit and aims to be as full-featured and offer the same performance as CUDA C. The toolchain is mature, has been under development since 2014, and can easily be installed on any current version of Julia using the integrated package manager. -
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CuArrays.jl
A Curious Cumulation of CUDA Cuisine
CuArrays provides a fully-functional GPU array, which can give significant speedups over normal arrays without code changes. CuArrays are implemented fully in Julia, making the implementation elegant and extremely generic. -
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Tiny
Tiny Face Detector, CVPR 2017
...It provides training/testing scripts, a demo (tiny_face_detector.m), model loading, evaluation on WIDER FACE, and supporting utilities (e.g. cnn_widerface_eval.m). The code depends on MatConvNet, which must be compiled (with GPU / CUDA / cuDNN support) for full performance. Pretrained model provided (ResNet101-based, plus alternatives). Demo and evaluation scripts for benchmark datasets. Use of “foveal descriptors” to incorporate context for low-resolution faces. Pretrained model provided (ResNet101-based, plus alternatives). -
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PMCGPU
Parallel simulators for Membrane Computing on the GPU
...The objective of this project (PMCGPU) is to bring together all the researchers working on the development of parallel simulators for P systems, specially those using the GPU (e.g. CUDA, OpenCL, etc). Other parallel platforms are also welcome (multicore and manycore, FPGAs, etc). This project has been initiated by the Research Group on Natural Computing (Department of Computer Science and Artificial Intelligence, University of Seville). PMCGPU was born inside the P-Lingua project, of the same research group. ... -
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SoliditySHA3Miner
All-in-one mixed multi-GPU (nVidia, AMD, Intel) & CPU miner
All-in-one mixed multi-GPU (nVidia, AMD, Intel) & CPU miner solves proof of work to mine supported EIP918 tokens in a single instance (with API). -
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xmrMiner
A CUDA based miner for Monero
An optimized Monero miner designed to maximize GPU mining efficiency. -
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Simple program that displays information about CUDA-enabled devices. The program is equipped with GPU performance test.
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This project, initially developed at University College London, contains programs to perform rigid, affine and non-linear registration of nifti or analyse images. Two versions of the algorithms are included, a CPU- and a GPU- (using CUDA) based implementation.
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Merlin.jl
Deep Learning for Julia
Merlin is a deep learning framework written in Julia. It aims to provide a fast, flexible and compact deep learning library for machine learning. Merlin is tested against Julia 1.0 on Linux, OS X, and Windows (x64). -
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Deepo
Set up deep learning environment in a single command line
Deepo is a series of Docker images that allows you to quickly set up your deep learning research environment, supports almost all commonly used deep learning frameworks, supports GPU acceleration (CUDA and cuDNN included), also works in CPU-only mode, and works on Linux (CPU version/GPU version), Windows (CPU version) and OS X (CPU version). Their Dockerfile generator that allows you to customize your own environment with Lego-like modules, and automatically resolves the dependencies for you. For users in China who may suffer from slow speeds when pulling the image from the public Docker registry, you can pull deepo images from the China registry mirror by specifying the full path, including the registry, in your docker pull command. ... -
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...The program can be invoked within MATLAB and any other scripting language. It can export the results in different formats including ASCII text “.txt” and MATLAB “.mat” format. The program can also utilize the power of GPU via CUDA. With a modern gaming card “NVidia GTX TITAN”, a speed increase of a factor of 100 times can be achieved. This makes NPP a powerful tool especially for simulating long waveguides.
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HVVR
Hierarchical visibility for VR and real time rendering of effects
HVVR (Hierarchical Visibility for Virtual Reality) is an optimized software raycaster. It implements a hybrid CPU/GPU raycaster, suited for real-time rendering of effects such as lens distortion. By default, raycaster.vcxproj is configured to build all .cu files to the compute_61,sm_61 target. If your GPU needs a different code-gen target, you can change this in the raycaster project properties. Some installations of the CUDA SDK and Visual Studio integration appear to have broken build dependency tracking. ... -
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CUDA-MEME
Ultrafast scalable motif discovery algorithm using GPU computing
mCUDA-MEME is a well-established ultrafast scalable motif discovery algorithm based on MEME (version 4.4.0) algorithm for multiple GPUs using a hybrid combination of CUDA, MPI and OpenMP parallel programming models. This algorithm is a further extension of CUDA-MEME (based on MEME version 3.5.4) with respect to accuracy and speed and has been tested on a GPU cluster with eight compute nodes and two Fermi-based Tesla S2050 (and Tesla-based Tesla S1070) quad-GPU computing systems, running the Linux OS with the MPICH2 library. ... -
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GPU Machine Learning Library. This library aims to provide machine learning researchers and practitioners with a high performance library by taking advantage of the GPU enormous computational power. The library is developed in C++ and CUDA.
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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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GPU3SNP
Exhaustive search for third order epistatic interactions using CUDA
GPU3SNP is a multi-GPU tool that exhaustively analyzes case-control datasets looking for 3-SNP combinations that present epistatic interaction. It provides a list with the combinations that have higher Mutual Information, which is used as measure for interaction. It is parallelized using CUDA and can exploit several GPUs in the same node/system. -
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SW#
smith waterman, gpu, cuda, alignment
SW# (swsharp) is a library for sequence alignment based on CUDA enabled GPUs. It utilizes Hirschbergs and Ukkonens algorithm for memory efficiency and additional speed up. The library is scalable for use with multiple GPUs. Some parts of the library utilize MPI for CUDA enabled clusters. This work has been supported in part by Croatian Science Foundation under the project UIP-11-2013-7353. Database search now... -
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cphcttoolbox
Cph CT Toolbox is a selection of Computed Tomography tools
Copenhagen Computed Tomography Toolbox is a collection of applications and libraries for flexible and efficient CT reconstruction. The toolbox apps generally take a set of projections (X-ray intensity measurements) and filter and back project them in order to recreate the image or volume that the projections represent. The project includes both mostly informative CPU implementations and highly efficient GPU implementations. Regular releases are hosted at the Python Package Index. -
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The GPU-BH tree solver is a FLASH4 module to calculate the gravitational potential using a CUDA device.
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DualSPHysics
C++/CUDA/OpenMP based Smoothed Particle Hydrodynamics (SPH) Solver
DualSPHysics is based on the Smoothed Particle Hydrodynamics method and can be downloaded from the official website (www.dual.sphysics.org). The code is developed to study free-surface flow phenomena where Eulerian methods can be difficult to apply, such as waves or impact of dam-breaks on off-shore structures. DualSPHysics is a set of C++, CUDA and Java codes based on the SPHysics FORTRAN project (www.sphysics.org) that are designed to deal with real-life engineering problems and are... -
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CUDA-Quicksort
CUDA-Quicksort: A GPU-based implementation of the quicksort algorithm
CUDA-quicksort is a GPU-based implementation of the quicksort algorithm. CUDA-quicksort is designed to exploit the power computing of modern NVIDIA GPUs. "Two GPU-based implementations of the quicksort were presented in literature: the GPU-quicksort, a compute-unified device architecture (CUDA) iterative implementation, and the CUDA dynamic parallel (CDP) quicksort, a recursive implementation provided by NVIDIA Corporation."[*]. ... -
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This project, developed at UCL London, provides code for tomographic reconstruction. NiftyRec is written in C and has Python and Matlab extensions. Computationally intensive functions have a GPU accelerated version based on CUDA.
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The objective of this project is to create a library of code for pricing financial derivatives products using CUDA to achieve GPU programming.
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A GUI interface to a tool for generating SSBumps (Self Shadowed Bump Maps). Includes a CUDA GPU rendering extension.
