LuisaCompute的开发者们大多来自Rendering领域,如果你的方向与之重合,那它会使非常好的选择。但如果你想做的是Simulation,并且可能会用上很多的cuda生态,那么LuisaCompute暂时不是最优选择。因为它的RHI层隔离了实际的后端(DX, CUDA, Vulkan, Metal)和前端Runtime。如果你想快速使用上CUDA的生态,你很有可能需要自己实...
创建一个临时目录/tmp/torch_extensions/cppcuda_tutorial，向该临时目录发出Ninja构建文件，将你的源文件...
Learn More Tutorials CUDA Developer Tools is a series of tutorial videos designed to get you started using NVIDIA Nsight™ tools for CUDA development. It explores key features for CUDA profiling, debugging, and optimizing. CUDA Compatibility ...
CUDA的特色如下,引自NVIDIA的官方说明: 1、为并行计算设计的统一硬件软件架构。有可能在G80系列上得到发挥。 2、在GPU内部实现数据缓存和多线程管理。这个强,思路有些类似于XB360 PS3上的CPU编程。 3、在GPU上可以使用标准C语言进行编写。 4、标准离散FFT库和BLAS基本线性代数计算库。 5、一套CUDA计算驱动。 6...
CUDA Developer Tools is a new tutorial video series for getting started with CUDA developer tools. Grow your skills, apply our examples to your own development environment, and stay updated on features and functionalities. The videos walk you through how to analyze performance reports, offer debuggi...
System-wide insights:NVIDIA Nsight Systemsprovides system-wide performance insights, visualization of CPU processes, GPU streams, and resource bottlenecks. It also traces APIs and libraries, helping developers locate optimization opportunities. CUDA kernel profiling:NVIDIA Nsight Computeenables detailed analysis...
CUDA 编程指南学习. Contribute to XinghangLiu/cuda-tutorial development by creating an account on GitHub.
This tutorial covers how to debug an application locally. This means that you will need to have theNVIDIA Nsighthost software running on a machine with Visual Studio, and have the Nsight Monitor also running on the same machine. Make sure that the machine you use meets the system requirements...
前段时间一直在做算子上的优化加速工作,在和其他同学的讨论中发现用Cuda编写算子存在一定的门槛。虽然知乎上有很多优秀的教学指南、PyTorch官方也给出了tutorial(具体地址会放在文章末尾),但是对于每个环节的介绍与踩坑点似乎没有详实的说明。 结合我当时入门...
CUDATutorial A CUDA tutorial to make people learn CUDA program from 0 test enviroment Turing T4 GPU compile command compile by hand nvcc xxx.cu -o xxx if that does not work, pls try: nvcc xxx.cu --gpu-architecture=compute_yy -o xxx xxx is file name, yy is GPU compute capability, ...