Build userspace NVMe drivers and storage applications with CUDA support

Ubuntu 18.04 How to install Nvidia driver + CUDA + CUDNN + build tensorflow for gpu step by step command line

Mavenized JCuda

Dockerized Folding@home client with NVIDIA GPU support to help battle COVID-19

NVIDIA DeepStream SDK

Implementation of ConjugateGradients method using C and Nvidia CUDA

An implementation of parallel exclusive scan in CUDA

这是一个基于NVIDIA cuda的开源程序，其中包括了二维和三维VTI介质正演模拟和逆时偏移成像，二维TTI介质逆时偏移成像，以及以上介质的ADCIGs提取[translation: This is an open source program based on NVIDIA cuda, which includes two-dimensional and three-dimensional VTI media forward simulation and reverse time migration imaging, two-dimensional TTI media reverse time migration imaging, and ADCIGs extraction of the above media]

HTML/JS port of CUDA Occupancy Calculator

基于NVIDIA的GPU加速的VTI介质有限差分正演地震模拟，[translation: NVIDIA-based GPU Accelerated Finite Difference Forward Seismic Simulation of VTI Media]

StiffMa: Fast finite element STIFFness MAtrix generation in MATLAB by using GPU computing.

FFmpeg 4.0 with NVIDIA P4 GPU Driver Support

PyTorch Image and Video Super-Resolution, specialized for vehicle and traffic view processing and performed by using Deep Convolutional Neural Networks

This system tracks artifacts in museum and triggers alarm if artifact goes missing from the frame.

My bachelor and master thesis at Voronezh State University

A simple image classifier built with Keras using NVIDIA cuda libraries.

CUDA miner project, compatible with most Maxwell/Pascal nVidia cards

GPUDirect Async implementation of HPGMG-FV CUDA

[WIP] Linear Boolean algebra primitives written in NVIDIA CUDA

Convolution 2D cuDNN C++ implement demo 二维卷积的cuDNN实现样例 2次元畳み込みのcuDNN実装例

Convolution 3D cuDNN C++ implement demo 三维卷积的cuDNN实现样例 3次元畳み込みのcuDNN実装例

Wrapper scripts to install proprietary NVIDIA drivers on GNU/Linux systems

This script collects some informations about NVLink and PCI bus traffic of NVidia GPUs. Results are published as prometheus metrics via a websocket.

Unofficial HTMLCOIN Mining Group

Recent development in Graphic Processing Units (GPUs) has opened a new challenge in harnessing their computing power as a new general-purpose computing paradigm with its CUDA parallel programming. However, porting applications to CUDA remains a challenge to average programmers. We have developed a restructuring software compiler (RT-CUDA) with best possible kernel optimizations to bridge the gap between high-level languages and the machine dependent CUDA environment. RT-CUDA is based upon a set of compiler optimizations. RT-CUDA takes a C-like program and convert it into an optimized CUDA kernel with user directives in a con.figuration .file for guiding the compiler. While the invocation of external libraries is not possible with OpenACC commercial compiler, RT-CUDA allows transparent invocation of the most optimized external math libraries like cuSparse and cuBLAS. For this, RT-CUDA uses interfacing APIs, error handling interpretation, and user transparent programming. This enables efficient design of linear algebra solvers (LAS). Evaluation of RT-CUDA has been performed on Tesla K20c GPU with a variety of basic linear algebra operators (M+, MM, MV, VV, etc.) as well as the programming of solvers of systems of linear equations like Jacobi and Conjugate Gradient. We obtained significant speedup over other compilers like OpenACC and GPGPU compilers. RT-CUDA facilitates the design of efficient parallel software for developing parallel simulators (reservoir simulators, molecular dynamics, etc.) which are critical for Oil & Gas industry. We expect RT-CUDA to be needed by many industries dealing with science and engineering simulation on massively parallel computers like NVIDIA GPUs.

Implementing High Performance Computation on General Mathematics Operation