|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/gpgpu-programming-using-cuda/
课程评论：没有评论
课程名称：使用CUDA进行GPGPU编程 课程概述：中央处理器（CPU）负责计算机或服务器上所有软件正常运行所需的任务，而图形处理器（GPU）则支持CPU执行并行计算。GPU能够以更快的速度完成简单和重复的任务，因为它可以将任务分解为更小的组件并同时完成。GPU最初是为了处理图像、视频游戏计算机图形和视觉数据而设计的。通用图形处理器（GPGPU）被采纳用于提升其他计算过程，如变换器和深度学习。近年来，人工智能推动了GPU张量核心的使用，这些核心相比传统核心实现了显著更高的吞吐量。课程包含超过160张信息丰富的幻灯片，以及多个使用NVIDIA CUDA并行计算平台和应用程序接口（API）的编程练习，允许软件开发人员利用GPGPU进行通用处理。此外，课程还包含简短的测验。 课程亮点： - 学习GPGPU内部架构 - 复习GPGPU擅长解决的科学问题 - 了解图形管道及构建场景的步骤 - 探讨GPGPU在神经网络和视频解码中的应用 - 学习GPGPU内存结构和优化技术 - 理解实用算法的并行化实现原则 - 能够编写C/C++、FORTRAN和MATLAB仿真代码，以在CUDA GPGPU上执行特定应用 - 熟悉CUDA GPGPU编程的特性和注意事项 关于讲师：讲师拥有6项美国视频专利，并已授权给行业，获得过美国全国广播协会技术创新奖，为先进媒体技术的演示做出了贡献。他为康卡斯特/NBC、高通、摩托罗拉、各大学和IEEE等机构提供了多个定制的视频课程，并曾担任有线电视和视频流媒体的专家证人。
The Central Processing Unit (CPU) handles all the tasks required for all software on the computer or server to run correctly. A Graphic Processing Unit (GPU), on the other hand, supports the CPU to perform concurrent calculations. A GPU can complete simple and repetitive tasks much faster because it can break the task down into smaller components and finish them in parallel. These cores were initially designed to process images, video game computer graphics, and visual data. General Purpose Graphic Processor Units (GPGPUs) were adopted to enhance other computational processes, such as transformers and deep learning. More recently, AI is driving GPU tensor cores that achieve significantly higher throughput compared to traditional cores. The course comprises over 160 informative slides with several programming exercises using the NVIDIA CUDA parallel computing platform and application programming interface (API) that allows software developers to use GPGPUs for general-purpose processing. It also contains a short quiz.Course HighlightsStudy GPGPU internal architectureReview scientific problems GPGPUs solve wellUnderstand graphics pipeline and steps to construct a sceneStudy how GPGPUs are applied to neural networks and video decodingLearn GPGPU memory structure and optimization techniques Learn principles of practical algorithms to parallelize an implementationBe able to write C/C++, FORTRAN, and MATLAB simulation code to execute on CUDA GPGPU for a specific applicationBe cognizant of CUDA GPGPU programming quirksAbout the InstructorThe instructor has developed 6 U.S. video patents that were licensed to industry and received the National Association of Broadcasters Technology Innovation Award for demonstrations of advanced media technologies. He has conducted many custom video courses for Comcast/NBC, Qualcomm, Motorola, universities, and the IEEE, and has served as an expert witness on cable TV and video streaming.
