Search Results for "cuda"
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Showing 33 open source projects for "cuda"
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Monte Carlo eXtreme (MCX)
Physically accurate and validated GPU ray-tracer
MCX is a GPU-accelerated, general-purpose, physically-accurate and feature-rich 3-D light transport simulator. It is one of the fastest simulators because it can use tens of thousands of GPU threads to simulate photons in parallel. -
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CIRT
CIRT - CUDA Interactive Ray Tracer
CIRT is an implementation of PRTP (Programmable Ray Tracing Pipeline). Mainly it is to be used as a ray-tracing equivalent of OpenGL. It allows the user to implement various ray-tracing related algorithms. -
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SAGECAL
GPU/MIC accelerated radio interferometric calibration program
SAGECal is a very fast, memory efficient and GPU accelerated radio interferometric calibration program. It can handle all source models including points, Gaussians and Shapelets. It can calibrate along hundreds of directions without running out of memory in almost real time. Intel Xeon Phi acceleration is also available. Distributed calibration using MPI and consensus optimization is enabled. Also tools to build/restore sky models are included. -
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GSvit
Fast FDTD solver with graphics card support
Fast FDTD solver with graphics card support. Optimized for nanoscale optics - scanning near field optical microscopy, rough surface scattering and solar cells. Uses CUDA environment for graphics card operation. -
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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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CUDALucas
A program that uses CUDA to accelerate the Lucas Lehmer test.
CUDALucas is a program implementing the Lucas-Lehmer primality test for Mersenne numbers using the Fast Fourier Transform implemented by nVidia's cuFFT library. You need a CUDA-capable nVidia card with compute compatibility >= 1.3 up to CUDA 6.5, 2.x up to 7.0 and 3.x for >=CUDA 8.0 . The program is run from the command line, however it can be configured without using a terminal. See the Wiki for more details. README for new users available at the Files/Downloads page. -
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CUDASW++: Smith-Waterman Algorithm
The fastest Smith-Waterman protein database search algorithm on GPUs
CUDASW++ software is a public open source software for Smith-Waterman protein database searches on Graphics Processing Units with CUDA. This software have been added to the NVIDIA Tesla Bio Workbench (http://www.nvidia.com/object/swplusplus_on_tesla.html -
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GPU MrBayes implements MrBayes MC(3) on the GPU using CUDA. When using our program in your article, please cite our paper "Efficient Implementation of MrBayes on multi-GPU" (http://mbe.oxfordjournals.org/content/early/2013/03/14/molbev.mst043.abstract.html?papetoc).
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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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viewpix
A Landsat 8 scene viewer
...Each Landsat 8 scene is roughly 190 X 180 kilometers. Viewpix was originally written as a simple platform to test GPU software on low end computer systems. This is the "lite" version of Viewpix as it has no cuda filters installed. Viewpix is written in C. It is tested on Ubuntu and Centos. It should work with most current Linux distributions. Viewpix runs efficiently on computers with modest CPU's such as low power mini-ITX boards. It is known to work nicely on Intel NUC, AMD A6 SOC , and NVIDIA TX2. A multiple core cpu, 8 GB ram, and a large capacity hard drive are suggested. -
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Barracuda is a high-speed sequence aligner based on Sanger's BWA and utilizes the latest Nvidia CUDA architecture for accelerating alignments of sequence reads generated by next-generation sequencers.
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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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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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MDcore is an Open-Source library implementing the core functionality of a Molecular Dynamics engine. The engine is inherently multi-threaded and runs efficiently on multi-processor/multi-core shared-memory architectures.
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MSA-CUDA: multiple sequence aligner
multiple sequence alignment on CUDA-enabled GPUs.
This project is not active any more since we failed to get the permit from the Clustal team to distribute our software. You can refer to the paper "Yongchao Liu, Bertil Schmidt, Douglas L Maskell:MSA-CUDA: multiple sequence alignment on graphics processing units with CUDA. ASAP 2009" for more details. -
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GPU Edge Detector
GPU Interactive Program For Edge detection
...(The code section is currently close until our article gets published) Commiting publicly to the project is currently closed but feel free to email us. The source code is dependant on CUDA 5.0 Samples now available as part of the CUDA Toolkit. "This software contains source code provided by NVIDIA Corporation." -
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cuLsoda is an implementation of the Livermore Solver for Ordinary Differential Equations with Automatic switching for stiff and non-stiff problems (LSODA), written in NVIDIA's C for CUDA GPGPU API. NOTE: This repository is now deprecated and replaced by a new one at GitHub: https://github.com/Celemourn/cuLsoda
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GPUmat
GPUmat is a C/C++ GPU engine for Matlab based on NVIDIA CUDA.
Please download Windows and Linux version by clicking on "Browse All Files". GPUmat allows standard MATLAB code to run on GPUs. The engine is written in C/C++ and based on NVIDIA CUDA. Please contact gpyougroup@gmail.com for any questions. Unfortunately GPUmat was compiled for CUDA 5.0 and we basically stopped any support for other CUDA version because we don't have the resources to do it. But you can compile the source code if you want. Instructions to compile the source code: You need to download a SVN client and then from command line: svn export http://svn.code.sf.net/p/gpumat/code/trunk .... -
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Rozkład temperatury w pomieszczeniach
Symulacja procesów cieplnych zachodzących w pomieszczeniach
...Piotr Fiertek Celem projektu jest stworzenie środowiska symulacyjnego dla procesów termicznych zachodzących w pomieszczeniach. Integralną jego częścią jest implementacja algorytmów obliczeniowych na procesorach graficznych GPU przy użyciu standardów CUDA oraz OpenCL. Innymi ważnymi elementami są graficzna wizualizacja wyników z wykorzystaniem biblioteki graficznej OpenGL oraz stworzenie interfejsu użytkownika umożliwiającego zmianę parametrów symulacji. -
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CUDA-SPHERE-FWD-MEEG
CUDA accelerated spherical model forward solution for EEG/MEG
CUDA-SPHERE-FWD-MEEG is a CUDA C based toolkit which provides a GPU based implementation of the spherical model forward solution for the 306 channel Elekta Neuromag MEG system and the EEG. The 1-Sphere forward solution for the MEG and the 4-Sphere forward solution for the EEG is implemented in CUDA C and an accelerated solution is obtained using the NVIDIA GPU when the solution is calculated for a large number of dipoles (on the order of 15000 and above) and sensor locations. ... -
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Oree - Optimum Real-time Estimation of Events of Interaction: real-time reconstruction of photon interaction events in Gamma Cameras. Optionally making use of a CUDA enable GPU, Oree achieves reconstruction of over 1M events/sec.
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A simple CUDA accelerated solver for general dimensional n-body problems. Currently only the conjugate gradient method has been implemented, but more will soon follow.
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The CUDA wrapper library provides means for an efficient resource sharing and resource protection on multi-user GPU clusters.It implements the following functionality: 1) Virtualization of the physical GPU devices 2) Ensuring NUMA affinity for GPUs
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simple game of life use gpu cuda
