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    "## N Ways to GPU Programming\n",
    "\n",
    "## Learning objectives\n",
    "With the release of CUDA in 2007, different approaches to programming GPUs have evolved. Each approach has its own advantages and disadvantages. By the end of this bootcamp session, students will have a broader perspective on GPU programming approaches to help them select a programming model that better fits their applications' needs and constraints. The bootcamp will teach how to accelerate a popular algorithm of Radial Distribution Function (RDF) using the following methods:\n",
    "* C++ Standard: pSTL\n",
    "* Directives: OpenACC, OpenMP\n",
    "* Frameworks: Kokkos\n",
    "* Lower level C Construct: CUDA C\n",
    "\n",
    "Let's start with testing the CUDA Driver and GPU you are running the code on in this lab:"
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "!nvidia-smi"
   ]
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   "source": [
    "### Tutorial Outline\n",
    "\n",
    "During this lab, we will be working on porting mini applications in MD and/or CFD domains to GPUs. You can choose to work with either of this application. Please click on one of the below links to start:\n",
    "\n",
    "- N Ways to GPU Programming in [MD](nways_MD/English/nways_MD_start.ipynb) domain\n"
   ]
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   "source": [
    "### Tutorial Duration\n",
    "The lab material will be presented in a 4hr session. Link to material is available for download at the end of the lab.\n",
    "\n",
    "### Content Level\n",
    "Beginner, Intermediate\n",
    "\n",
    "### Target Audience and Prerequisites\n",
    "The target audience for this lab is ..\n",
    "\n",
    "--- \n",
    "\n",
    "## Licensing \n",
    "\n",
    "This material is released by NVIDIA Corporation under the Creative Commons Attribution 4.0 International (CC BY 4.0). "
   ]
  }
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