Topic: nvcc Goto Github
Some thing interesting about nvcc
Some thing interesting about nvcc
nvcc,Nvidia NVCC CUDA programs for begineers.
User: alpha74
nvcc,Hungarian Algorithm for Linear Assignment Problem implemented using CUDA.
User: alpha74
nvcc,Tutorial to install NVIDIA Drivers, CUDA 11.4 and cuDNN for deep learning programming on Ubuntu 20.04.
User: ashutoshiitk
nvcc,bilibili视频【CUDA 12.1 并行编程入门(C++语言版)】配套代码
User: codingonion
Home Page: https://www.bilibili.com/video/BV1Sj411H7Qq/
nvcc,bilibili视频【CUDA 12.1 并行编程入门(Python语言版)】配套代码
User: codingonion
Home Page: https://www.bilibili.com/video/BV1db4y1P7iX/
nvcc,bilibili视频【CUDA 12.1 并行编程入门(Rust语言版)】配套代码
User: codingonion
Home Page: https://www.bilibili.com/video/BV18e411H7bY/
nvcc,Implementação simples do Perceptron Multicamadas em CUDA.
User: gilbertobastos
nvcc,this repository contains the various programs that can written using CUDA Toolkit.
User: gvvsnrnaveen
nvcc,A barebones 3D renderer in C++ and Python
User: iamsubhranil
nvcc,Gradle plugin for integrating Cuda's nvcc tool
User: jbcbezerra
Home Page: https://plugins.gradle.org/plugin/io.github.jbcbezerra.nvcc
nvcc,Guidance for Nvidia Jetson Nano
Organization: kangyolo
nvcc,Problems of June day to day challenge in Leetcode
User: manikandan-ravikiran
nvcc,Lightning fast NVCC course equivalencies
User: mattbdean
Home Page: https://www.novaxfer.io
nvcc,This repository contains various CUDA C programs demonstrating parallel computing techniques using NVIDIA's CUDA platform.
User: minnukota381
nvcc,Parallel Heterogeneous CPU/GPU computing
User: neudinger
nvcc,Autotuning NVCC Compiler Parameters, published @ CCPE Journal
User: phrb
nvcc,Resources for autotuning CUDA compiler parameters
User: phrb
nvcc,Solutions to assignment given in the class of CO316
User: pockemon
nvcc,This is my thesis work for the Bachelor's degree in Physics. / Este es mi trabajo de titulación para la Licenciatura en Física.
User: rodolfo-gallegos
nvcc,vector calculation with GPU acceleration using CUDA
User: sahil-rajwar-2004
nvcc,Case studies constitute a modern interdisciplinary and valuable teaching practice which plays a critical and fundamental role in the development of new skills and the formation of new knowledge. This research studies the behavior and performance of two interdisciplinary and widely adopted scientific kernels, a Fast Fourier Transform and Matrix Multiplication. Both routines are implemented in the two current most popular many-core programming models CUDA and OpenACC. A Fast Fourier Transform (FFT) samples a signal over a period of time and divides it into its frequency components, computing the Discrete Fourier Transform (DFT) of a sequence. Unlike the traditional approach to computing a DFT, FFT algorithms reduce the complexity of the problem from O(n2) to O(nLog2n). Matrix multiplication is a cornerstone routine in Mathematics, Artificial Intelligence and Machine Learning. This research also shows that the nature of the problem plays a crucial role in determining what many-core model will provide the highest benefit in performance.
User: shadyboukhary
nvcc,creating a personal neural network library in cuda by as a learning stage side by side project
User: theunsolveddev
nvcc,Example Makefile for CUDA and C++ source files in a standard project layout.
User: traviswthompson1
nvcc,A python script which helps visualize the sorting routine of bitonic sort (executed in parallel using nvcc).
User: underscoreanuj
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