Take content from the various papers written and put it in the documentation.

**graph_framework**: A graph computation framework that supports auto 

differentiation. It is designed to allow domain scientists to create cross 

platform GPU accelerated code and embed those codes in existing legacy tools.

platform GPU accelerated code and embed those codes in existing legacy tools. It

is designed for the domain of physics problems where there same physics is 

applied to a large ensemble of independent systems.

This framework enables:

1. Portability to Nvidia, AMD, and Apple GPUs and CPUs.

2. Abstraction of the physics from the compute.

3. Auto Differentiation.

4. Embedding in C, C++, and Fortran codes. 

The compute kernels created have strong scaling to multiple devices

![Strong Scaling](graph_docs/StrongScaling.png)

and the best throughput on both GPUs and CPUs compared to other frameworks like

[MLX](https://ml-explore.github.io/mlx/build/html/index.html) and 

[JAX](https://docs.jax.dev/en/latest/)

![Throughput](graph_docs/Comparison.png)

[![Continuous Integration Test](https://github.com/ORNL-Fusion/graph_framework/actions/workflows/ci_test.yaml/badge.svg?branch=main)](https://github.com/ORNL-Fusion/graph_framework/actions/workflows/ci_test.yaml)

For instructions to build the code consult the 

[build system](https://ornl-fusion.github.io/graph_framework-docs/build_system.html)

documentation. This framework uses a [cmake](https://cmake.org) based build 

system and requires the [NetCDF-C](https://www.unidata.ucar.edu/software/netcdf/)

library.

system and requires the 

[NetCDF-C](https://www.unidata.ucar.edu/software/netcdf/) library.