<jupyter_start><jupyter_text>N Ways to GPU Programming Learning ObjectivesWith the release of NVIDIA CUDA in 2007, different approaches to GPU programming have evolved. Each approach has its own advantages and disadvantages. By the end of this bootcamp session, participants will have a broader perspective on GPU programming approaches to help them select a programming model that better fits their application's needs and constraints. The bootcamp will teach how to accelerate a real-world scientific application using the following methods:* Standard: C++ stdpar, Fortran Do-Concurrent* Directives: OpenACC, OpenMP* Frameworks: Kokkos* Programming Language Extension: CUDA C, CUDA Fortran, Python CuPy, Python NumbaLet's start by testing the CUDA Driver and GPU you are running the code on in this lab:<jupyter_code>!nvidia-smi<jupyter_output><empty_output>
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