Search Results for "cuda" - Page 3
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Showing 67 open source projects for "cuda"
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OpenCV CUDA Binaries
OpenCV Pre-built CUDA binaries
... - https://www.nuget.org/packages/opencvcuda-release/ - https://www.nuget.org/packages/opencvcuda-debug/ Packages for Release and Debug configurations (due to file size limitations on nuget.org) built with CUDA Toolkit 7.0.0 targeting architectures 2.0, 2.1, 3.0, 3.5, 3.7, 5.0, 5.2 suitable for Visual Studio 2013. -
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ViennaCL
Linear algebra and solver library using CUDA, OpenCL, and OpenMP
ViennaCL provides high level C++ interfaces for linear algebra routines on CPUs and GPUs using CUDA, OpenCL, and OpenMP. The focus is on generic implementations of iterative solvers often used for large linear systems and simple integration into existing projects. -
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OpenGL Mathematics (GLM) is a C++ mathematics library for 3D software based on the OpenGL Shading Language (GLSL) specification.
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MXLib is a C++ wrapper around the Intel® Integrated Performance Primitives (IPP) library and NVidia NPP CUDA library. You can use either IPP code (or a subset of functions that do not require IPP) on the CPU side, or use NPP/CUDA on the GPU side, or use both together. The function syntax is similar to that found in MatLab and the library is designed to make it easy to port your code from MatLab to C++. The idea is to provide Scientists, Engineers, Researchers and other non full-time programmers an easy to use, high performance library of functions.
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CURRENNT
CUDA-enabled machine learning library for recurrent neural networks
CURRENNT is a machine learning library for Recurrent Neural Networks (RNNs) which uses NVIDIA graphics cards to accelerate the computations. The library implements uni- and bidirectional Long Short-Term Memory (LSTM) architectures and supports deep networks as well as very large data sets that do not fit into main memory. -
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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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hiCUDA is a directive-based abstraction that simplifies CUDA programming. This project aims to develop a source-to-source compiler, based on Open64, that translates a sequential program with hiCUDA directives into an equivalent CUDA program.
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The Open64 Compiler and Tools site is dedicated to the continued development of the former SGI Pro64(TM) compiler for the IA64, x86, CUDA and MIPS architecture.
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This project is for CUDA bindings in F95/2003 using the Fortran 2003 ISO_C_BINDINGS module. It is intended to give near native call syntax to the CUDA SDK in Fortran 2003. Currently, most Fortran compilers are supporting the ISO_C_BINDINGS module.
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This project is a Neural Network Training Library implemented on CUDA. It's compatible with the most used libraries but allows to exploit the full power of NVIDIA graphic cards. Experimental results show speed ups over 100 times against CPU libraries
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Fossa is a runtime CUDA memory extender for Linux. With fossa, users can run CUDA applications that require more than the available amount of GPU memory. Fossa is targeted toward command line driven applications.
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Allows CUDA developers to deploy kernels requiring more global GPU memory than is available by transparently mapping pinned CPU memory into the GPU memory map. Works behind the scenes and is easily added to existing projects with minimal effort.
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cudaHashMap provides a insert-only hash map on CUDA. It is part of the MAPP project (https://sourceforge.net/projects/mapp/).
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A fast JPEG2000 encoder running on CUDA-capable GPUs. Supports lossless and lossy encoding. Both commandline tool with GUI and CUDA/C++ library. Visit >> http://cuj2k.sourceforge.net << for further information.
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nVidia CUDA and MPI python wrappers. These wrappers are written in pure C no swig or boost necessary. The CUDA wrapper exposes the CUDA runtime and Driver API's.
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A data parallel scientific programming model. Compiles efficiently to different platforms like distributed memory (MPI), shared memory multi-processor (pthreads), Cell BE processor, Nvidia Cuda, SIMD vectorization (SSE, Altivec), and sequential C++ code.
