Be the first to like this
Software Development Managers, Software Engineers, Developers, Requirements Engineers, Requirements Analysts Test Managers, Test Engineers, Testers.
GPGPUs have recently been utilized in various domains, including high performance computing and computational finance targeted for numerous end applications. They can be regarded as massively parallel processors with multiple-time faster computation and higher memory bandwidth compared to CPU. The speed up and lower cost from this system means that business critical computation can be performed in real-time and not rely on overnight computation jobs for data analysis.
This course will cover in-depth topics of both GPU architecture and CUDA Programming models. It also includes basic and advance techniques of CUDA programming for NVIDIA GPGPU cards with profiling methods discussed. This will lead to using CUDA and integrate it with your system to achieve the best performance by utilizing the ultra-speed big data processing.
Upon completion, participants should be able to demonstrate each of the following:
i. Understand the technique of utilizing GPGPU architecture in your own system
ii. Ability to program CUDA and understand parallel programming.