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-# libkvikio
+# Welcome to KvikIO's C++ documentation!
 
-libkvikio is a C++ header-only library providing bindings to
-cuFile, which enables GPUDirectStorage (GDS).
+KvikIO is a Python and C++ library for high performance file IO. It provides C++ and Python
+bindings to [cuFile](https://docs.nvidia.com/gpudirect-storage/api-reference-guide/index.html)
+which enables [GPUDirect Storage (GDS)](https://developer.nvidia.com/blog/gpudirect-storage/).
+KvikIO also works efficiently when GDS isn't available and can read/write both host and device data seamlessly.
+
+KvikIO C++ is a header-only library that is part of the [RAPIDS](https://rapids.ai/) suite of open-source software libraries for GPU-accelerated data science.
+
+---
+**Notice** this is the documentation for the C++ library. For the Python documentation of KvikIO, see under **KvikIO**.
+
+---
+
+## Features
+
+* Object Oriented API.
+* Exception handling.
+* Concurrent reads and writes using an internal thread pool.
+* Non-blocking API.
+* Handle both host and device IO seamlessly.
+
+## Installation
+
+KvikIO is a header-only library and as such doesn't need installation.
+However, for convenience we release Conda packages that makes it easy
+to include KvikIO in your CMake projects.
+
+### Conda/Mamba
+
+We strongly recommend to use `mamba <https://github.com/mamba-org/mamba>`_ inplace of conda, which we will do throughout the documents.
+
+Install the **stable release** from the ``rapidsai`` channel like:
+```sh
+# Install in existing environment
+mamba install -c rapidsai -c conda-forge libkvikio
+# Create new environment (CUDA 11.8)
+mamba create -n libkvikio-env -c rapidsai -c conda-forge cuda-version=11.8 libkvikio
+# Create new environment (CUDA 12.0)
+mamba create -n libkvikio-env -c rapidsai -c conda-forge cuda-version=12.0 libkvikio
+```
+
+Install the **nightly release** from the ``rapidsai-nightly`` channel like:
+
+```sh
+# Install in existing environment
+mamba install -c rapidsai-nightly -c conda-forge libkvikio
+# Create new environment (CUDA 11.8)
+mamba create -n libkvikio-env -c rapidsai-nightly -c conda-forge python=3.10 cuda-version=11.8 libkvikio
+# Create new environment (CUDA 12.0)
+mamba create -n libkvikio-env -c rapidsai-nightly -c conda-forge python=3.10 cuda-version=12.0 libkvikio
+```
+
+---
+**Notice** if the nightly install doesn't work, set ``channel_priority: flexible`` in your ``.condarc``.
+
+---
+
+
+### Build from source
+
+To build the C++ example run:
+
+```
+./build.sh libkvikio
+```
+
+Then run the example:
+
+```
+./examples/basic_io
+```
+
+
+## Example
+
+```cpp
+#include <cstddef>
+#include <cuda_runtime.h>
+#include <kvikio/file_handle.hpp>
+using namespace std;
+
+int main()
+{
+  // Create two arrays `a` and `b`
+  constexpr std::size_t size = 100;
+  void *a = nullptr;
+  void *b = nullptr;
+  cudaMalloc(&a, size);
+  cudaMalloc(&b, size);
+
+  // Write `a` to file
+  kvikio::FileHandle fw("test-file", "w");
+  size_t written = fw.write(a, size);
+  fw.close();
+
+  // Read file into `b`
+  kvikio::FileHandle fr("test-file", "r");
+  size_t read = fr.read(b, size);
+  fr.close();
+
+  // Read file into `b` in parallel using 16 threads
+  kvikio::default_thread_pool::reset(16);
+  {
+    kvikio::FileHandle f("test-file", "r");
+    future<size_t> future = f.pread(b_dev, sizeof(a), 0);  // Non-blocking
+    size_t read = future.get(); // Blocking
+    // Notice, `f` closes automatically on destruction.
+  }
+}
+```