GPU Programming with CUDA

GPU-accelerated computing drives current scientific research. Writing fast numeric algorithms for GPUs offers high application performance by offloading compute-intensive portions of the code to an NVIDIA GPU. The course will cover basic aspects of GPU architectures and programming. Focus is on the usage of the parallel programming language CUDA-C which allows maximum control of NVIDIA GPU hardware. Examples of increasing complexity will be used to demonstrate optimization and tuning of scientific applications. Topics covered will include: Introduction to GPU/Parallel computing Programming model CUDA GPU libraries like CuBLAS and CuFFT Tools for debugging and profiling Performance optimizations Prerequisites: Some knowledge about Linux, e.g. make, command line editor, Linux shell, experience in C/C++https://juser.fz-juelich.de/record/90361