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managedCUDA makes the CUDA Driver API available in .net applications written in C#, Visual Basic or any other .net language. It also includes classes for an easy handling and interop with CUDA, i.e. build-in CUDA types like float3.

http://managedcuda.codeplex.com/Tags | amazing cuda gpgpu gpu managedcuda |

Implementation | DotNet |

License | LGPL |

Platform | Windows |

This project is a test of ManagedCuda and graphics interop to OpenTK to simulate a simple galaxy on the GPU.

⚠ Please note that while Emu 0.2.0 is quite usable, it suffers from 2 key issues. It firstly does nothing to minimize CPU-GPU data transfer and secondly it's compiler is not well-tested. These can be reasons not to use Emu 0.2.0. A new version of Emu is in the works, however, with significant improvements in the language, compiler, and compile-time checker. This new version of Emu should be released some time in Q4 of 2019. But unlike OpenCL/CUDA/Halide/Futhark, Emu is embedded in Rust. This lets it take advantage of the ecosystem in ways...

emu gpu gpgpu gpu-computing gpu-acceleration gpu-programmingArraymancer is a tensor (N-dimensional array) project in Nim. The main focus is providing a fast and ergonomic CPU, Cuda and OpenCL ndarray library on which to build a scientific computing and in particular a deep learning ecosystem. The library is inspired by Numpy and PyTorch. The library provides ergonomics very similar to Numpy, Julia and Matlab but is fully parallel and significantly faster than those libraries. It is also faster than C-based Torch.

tensor nim multidimensional-arrays cuda deep-learning machine-learning cudnn high-performance-computing gpu-computing matrix-library neural-networks parallel-computing openmp linear-algebra ndarray opencl gpgpu iot automatic-differentiation autogradNeanderthal is a Clojure library for fast matrix and linear algebra computations based on the highly optimized native libraries of BLAS and LAPACK computation routines for both CPU and GPU.. Read the documentation at Neanderthal Web Site.

clojure-library matrix gpu gpu-computing gpgpu opencl cuda high-performance-computing vectorization api matrix-factorization matrix-multiplication matrix-functions matrix-calculationsVexCL is a vector expression template library for OpenCL/CUDA. It has been created for ease of GPGPU development with C++. VexCL strives to reduce amount of boilerplate code needed to develop GPGPU applications. The library provides convenient and intuitive notation for vector arithmetic, reduction, sparse matrix-vector products, etc. Multi-device and even multi-platform computations are supported. The source code of the library is distributed under very permissive MIT license.

opencl cuda c-plus-plus gpgpu scientific-computing cpp11Gunrock is a CUDA library for graph-processing designed specifically for the GPU. It uses a high-level, bulk-synchronous, data-centric abstraction focused on operations on a vertex or edge frontier. Gunrock achieves a balance between performance and expressiveness by coupling high performance GPU computing primitives and optimization strategies with a high-level programming model that allows programmers to quickly develop new graph primitives with small code size and minimal GPU programming knowledge. For more details, please visit our website, read Why Gunrock, our TOPC 2017 paper Gunrock: GPU Graph Analytics, look at our results, and find more details in our publications. See Release Notes to keep up with the our latest changes.

gunrock cuda graph-processing graph-analytics gpu graph-primitivesA wrapper for NVidia's CuBLAS (Compute Unified Basic Linear Algebra Subprograms) for the CLR.

blas cuda gpgpu gpu hpc linear-algebra mathOptix.NET is a .NET wrapper for the Nvidia Optix GPU ray-tracing library.

cuda gpgpu optix realtime-radiosityNyuzi is an experimental GPGPU processor hardware design focused on compute intensive tasks. It is optimized for use cases like blockchain mining, deep learning, and autonomous driving. This project includes a synthesizable hardware design written in System Verilog, an instruction set emulator, an LLVM based C/C++ compiler, software libraries, and tests. It can be used to experiment with microarchitectural and instruction set design tradeoffs.

fpga gpu-computing gpu verilog hardware microprocessor graphics processor-architectureturbo.js is a small library that makes it easier to perform complex calculations that can be done in parallel. The actual calculation performed (the kernel executed) uses the GPU for execution. This enables you to work on an array of values all at once. turbo.js is compatible with all browsers (even IE when not using ES6 template strings) and most desktop and mobile GPUs.

glsl gpu vector simd calculations shaders gpgpu parallel webglscikit-cuda provides Python interfaces to many of the functions in the CUDA device/runtime, CUBLAS, CUFFT, and CUSOLVER libraries distributed as part of NVIDIA's CUDA Programming Toolkit, as well as interfaces to select functions in the CULA Dense Toolkit. Both low-level wrapper functions similar to their C counterparts and high-level functions comparable to those in NumPy and Scipy are provided. Package documentation is available at http://scikit-cuda.readthedocs.org/. Many of the high-level functions have examples in their docstrings. More illustrations of how to use both the wrappers and high-level functions can be found in the demos/ and tests/ subdirectories.

gpu cuda blas lapack numericalThis is the fourth version of a SIFT (Scale Invariant Feature Transform) implementation using CUDA for GPUs from NVidia. The first version is from 2007 and GPUs have evolved since then. This version is slightly more precise and considerably faster than the previous versions and has been optimized for Kepler and later generations of GPUs. On a GTX 1060 GPU the code takes about 1.6 ms on a 1280x960 pixel image and 2.4 ms on a 1920x1080 pixel image. There is also code for brute-force matching of features that takes about 2.2 ms for two sets of around 1900 SIFT features each.

gpu nvidia cuda sift visionMShadow is a lightweight CPU/GPU Matrix/Tensor Template Library in C++/CUDA. The goal of mshadow is to support efficient, device invariant and simple tensor library for machine learning project that aims for maximum performance and control, while also emphasize simplicity.MShadow also provides interface that allows writing Multi-GPU and distributed deep learning programs in an easy and unified way.

Build applications written in NVIDIA® CUDA™ code for OpenCL™ 1.2 devices. Other systems should work too, ideally. You will need at a minimum at least one OpenCL-enabled GPU, and appropriate OpenCL drivers installed, for the GPU. Both linux and Mac systems stand a reasonable chance of working ok.

opencl gpu coriander mac radeon intel amd nvidia ubuntuGPU accelerated handwritten digit recognition with regl. Note that this network will probably be slower than the corresponding network implemented on the CPU. This is because of the overhead associated with transferring data to and from the GPU. But in the future we will attempt implementing more complex networks in the browser, such as Neural Style, and then we think that we will see a significant speedup compared to the CPU.

regl cnn digit-recognition demo gpu webgl convolutional-neural-networks gpgpu deep-learning glsl digit recognition mnist convolutional neural network networksThis is a simple demo that demonstrates how to use Vulkan for compute operations only. In other words, this demo does nothing related to graphics, and only uses Vulkan to execute some computation on the GPU. For this demo, Vulkan is used to render the Mandelbrot set on the GPU. The demo is very simple, and is only ~400LOC. The code is heavily commented, so it should be useful for people interested in learning Vulkan. The application launches a compute shader that renders the mandelbrot set, by rendering it into a storage buffer. The storage buffer is then read from the GPU, and saved as .png. Check the source code comments for further info.

vulkan example compute gpgpu tutorial glsl spirv fractal mandelbrotBoost.Compute is a GPU/parallel-computing library for C++ based on OpenCL. The core library is a thin C++ wrapper over the OpenCL API and provides access to compute devices, contexts, command queues and memory buffers.

opencl boost c-plus-plus cpp compute gpu gpgpu performance hpcMake images smaller, resizing and resampling with incredible performance, scalability and ease with features such as GPGPU processing and distributed computing.

convert-images distributed-compute gpu image-resizer resize-all-imagesA Clojure Library for Bayesian Data Analysis and Machine Learning on the GPU. Distributed under the Eclipse Public License either version 1.0 or (at your option) any later version.

bayesian-inference bayesian-data-analysis gpu-computing gpu-acceleration statistics machine-learning clojure-library bayesian opencl cuda high-performance-computing gpu mcmc markov-chain-monte-carlo
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