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tutorials/0_prerequisites.md

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+# Prerequisites
+
+To get started programming with CUDA, download and install the [CUDA Toolkit and developer driver](https://developer.nvidia.com/cuda-downloads). The toolkit includes nvcc, the NVIDIA CUDA Compiler, and other software necessary to develop CUDA applications. The driver ensures that GPU programs run correctly on CUDA-capable hardware, which you'll also need.
+
+You can confirm that the CUDA Toolkit is correctly installed on your machine by running `nvcc --version` from a command line. For example, on a Linux machine,
+
+```
+$ nvcc --version
+nvcc: NVIDIA (R) Cuda compiler driver
+Copyright (c) 2005-2022 NVIDIA Corporation
+Built on Tue_May__3_18:49:52_PDT_2022
+Cuda compilation tools, release 11.7, V11.7.64
+Build cuda_11.7.r11.7/compiler.31294372_0
+```
+
+outputs the compiler information. If the previous command was not successful, then the CUDA Toolkit is likely not installed, or the path to nvcc (`C:\CUDA\bin` on Windows machines, `/usr/local/cuda/bin` elsewhere) is not part of your `PATH` environment variable.
+
+Additionally, you'll also need a host compiler which works with `nvcc` to compile and build CUDA programs. On Windows, this is `cl.exe`, the Microsoft compiler, which ships with Microsoft Visual Studio. On POSIX OSes, this is `gcc` or `g++`. The official [CUDA Quick Start Guide](https://docs.nvidia.com/cuda/cuda-quick-start-guide/index.html) can tell you which compiler versions are supported on your particular platform.
+
+To make sure everything is set up correctly, let's compile and run a trivial CUDA program to ensure all the tools work together correctly.
+
+```
+#include <stdio.h>
+
+__global__ void foo()
+{
+}
+
+int main()
+{
+  foo<<<1,1>>>();
+
+  printf("CUDA error: %s\n", cudaGetErrorString(cudaGetLastError()));
+
+  return 0;
+}
+```
+
+To compile this program, copy it to a file called `test.cu` and compile it from the command line. For example, on a Linux system, the following should work:
+
+```
+$ nvcc test.cu -o test
+$ ./test CUDA error: no error
+```
+
+If the program succeeds without error, then let's start coding!
+

tutorials/README.md

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+# Introduction
+
+This tutorial describes how to get started programming in CUDA C/C++, a language for programming massively parallel GPUs. It's intended for an audience experienced in C or C++ programming.
+
+Let's begin by making sure our development system meets the [prerequisites for CUDA programming](0_prerequisites.md).
+