Free Download Udemy - Gpgpu Programming Using Cuda
Published: 2/2025
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 872.52 MB | Duration: 1h 25m
A Comprehensive Guide
What you'll learn
Study GPGPU internal architecture
Review scientific problems GPGPUs solve well
Understand graphics pipeline and steps to construct a scene
Study how GPGPUs are applied to neural networks and video decoding
Learn GPGPU memory structure and optimization techniques
Learn principles of practical algorithms to parallelize an implementation
Be able to write C/C++, Fortran, and MATLAB simulation code to execute on CUDA GPGPU for a specific application
Be cognizant of CUDA GPGPU programming quirks
Requirements
Some programming experience.
Description
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 150 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.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 quirks
Anyone who is interested in CUDA GPU programming.
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