This title appears in the Scientific Report :
2022
Please use the identifier:
http://hdl.handle.net/2128/33226 in citations.
GPU Programming with CUDA
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 covers basic aspects of GPU architectures and programming. Focus is on the usag...
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Personal Name(s): | Herten, Andreas (Corresponding author) |
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Meinke, Jan (Corresponding author) / Haghighi Mood, Kaveh / Kraus, Jiri / Hrywniak, Markus | |
Contributing Institute: |
Jülich Supercomputing Center; JSC |
Imprint: |
2022
|
Conference: | PRACE Training Course at JSC, online 2022-04-25 - 2022-04-29 |
Document Type: |
Lecture |
Research Program: |
PRACE 6th Implementation Phase Project Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups |
Link: |
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Publikationsportal JuSER |
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 covers basic aspects of GPU architectures and programming. Focus is on the usage of the parallel programming language CUDA C/C++ which allows maximum control of NVIDIA GPU hardware. Examples of increasing complexity are 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; Advanced GPU programming model; CUDA Fortran in a nutshell.This course is a PRACE training course. |