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所在平台: Udemy |
课程主页: https://www.udemy.com/course/learn-cuda/
课程评论：没有评论
课程名称：使用 Docker 学习 CUDA！ 课程概述： 本课程提供一种全新的方式，让你无需 NVIDIA GPU 即可学习 CUDA。你可以在你的笔记本电脑、平板电脑甚至手机上学习 CUDA 编程。CUDA 是一个通用的编程模型，可以让你利用现代 GPU 的强大计算能力，以及用于机器学习、图像处理、线性代数和并行算法的强大库。 学习内容： 课程将演示如何利用 Docker 和操作系统级虚拟化技术（将软件打包成称为容器）以及 GPGPU-Sim（一种模拟支持 CUDA 或 OpenCL 的 GPU 计算工作负载的图形处理单元的周期级模拟器）来学习 CUDA。本课程旨在以易于理解的方式介绍 NVIDIA 的 CUDA 并行架构和编程模型。课程内容将根据反馈每月更新，并增加新的课程和练习。 具体学习主题包括： * 虚拟化基础 * Docker 基础 * GPU 基础 * CUDA 安装 * CUDA Toolkit * CUDA 线程和块的各种组合 * CUDA 编码示例 此外，课程还将通过 Zoom 直播系列讲座，深入探讨并行与分布式计算和高性能计算 (HPC) 系统软件栈的各个方面，包括 Slurm、PBS Pro、OpenMP、MPI 和 CUDA。直播课程将在 Scientific Programming School 进行，这是一个交互式且先进的科学编程电子学习平台。购买本课程的学生将免费获得 Scientific Programming School（SCIENTIFIC PROGRAMMING IO）的交互式版本（包含科学代码游乐场）。加入方式将在奖励内容部分提供。 免责声明： 课程中使用的部分图片版权归 NVIDIA 所有。
WELCOME!We present you the long waited approach to Learn CUDA WITHOUT NVIDIA GPUS! Finally, you can learn CUDA just on your laptop, tablet or even on your mobile, and that's it! CUDA provides a general-purpose programming model which gives you access to the tremendous computational power of modern GPUs, as well as powerful libraries for machine learning, image processing, linear algebra, and parallel algorithms.WHAT DO YOU LEARN?We will demonstrate how you can learn CUDA with the simple use of Docker and OS-level virtualization to deliver software in packages called containers and GPGPU-Sim, a cycle-level simulator modeling contemporary graphics processing units (GPUs) running GPU computing workloads written in CUDA or OpenCL. This course aims to introduce you with the NVIDIA's CUDA parallel architecture and programming model in an easy-to-understand way. We plan to update the lessons and add more lessons and exercises every month!Virtualization basics Docker EssentialsGPU BasicsCUDA InstallationCUDA ToolkitCUDA Threads and Blocks in various combinationsCUDA Coding ExamplesBased on your earlier feedback, we are introducing a Zoom live class lecture series on this course through which we will explain different aspects of the Parallel and distributed computing and the High Performance Computing (HPC) systems software stack: Slurm, PBS Pro, OpenMP, MPI and CUDA! Live classes will be delivered through the Scientific Programming School, which is an interactive and advanced e-learning platform for learning scientific coding. Students purchasing this course will receive free access to the interactive version (with Scientific code playgrounds) of this course from the Scientific Programming School (SCIENTIFIC PROGRAMMING IO). Instructions to join are given in the bonus content section.DISCLAIMERSome of the images used in this course are copyrighted to NVIDIA.
