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README.md

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 | [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) | [![doc](https://img.shields.io/badge/docs-dev-blue.svg)](https://sshin23.github.io/ExaModels.jl/)  | [![build](https://github.com/sshin23/ExaModels.jl/actions/workflows/test.yml/badge.svg)](https://github.com/sshin23/ExaModels.jl/actions/workflows/test.yml) | [![codecov](https://codecov.io/gh/sshin23/ExaModels.jl/branch/main/graph/badge.svg?token=8ViJWBWnZt)](https://codecov.io/gh/sshin23/ExaModels.jl) |
 
 ## Introduction
-ExaModels.jl employs what we call **SIMD abstraction for nonlinear programs** (NLPs), which allows for the **preservation of the parallelizable structure** within the model equations, facilitating **efficient, parallel derivative evaluations** on the **GPU**.
+ExaModels.jl employs what we call **[SIMD](https://en.wikipedia.org/wiki/Single_instruction,_multiple_data) abstraction for [nonlinear programs](https://en.wikipedia.org/wiki/Nonlinear_programming)** (NLPs), which allows for the **preservation of the parallelizable structure** within the model equations, facilitating **efficient, parallel derivative evaluations** on the **[GPU](https://en.wikipedia.org/wiki/Graphics_processing_unit)**.
 
-ExaModels.jl is different from other algebraic modeling tools, such as JuMP or AMPL, in the following ways:
-- **Modeling Interface**: ExaModels.jl enforces users to specify the model equations always in the form of `Iterable`s. This allows ExaModels.jl to preserve the SIMD-compatible structure in the model equations.
+ExaModels.jl is different from other algebraic modeling tools, such as [JuMP](https://github.com/jump-dev/JuMP.jl) or [AMPL](https://ampl.com/), in the following ways:
+- **Modeling Interface**: ExaModels.jl enforces users to specify the model equations always in the form of `Generator`s. This allows ExaModels.jl to preserve the SIMD-compatible structure in the model equations.
 - **Performance**: ExaModels.jl compiles (via Julia's compiler) derivative evaluation codes that are specific to each computation pattern, based on reverse-mode automatic differentiation. This makes the speed of derivative evaluation (even on the CPU) significantly faster than other existing tools.
-- **Portability**: ExaModels.jl can evaluate derivatives on GPU accelerators. The code is currently only tested for NVIDIA GPUs, but GPU code is implemented mostly based on the portable programming paradigm, KernelAbstractions.jl. In the future, we are interested in supporting Intel, AMD, and Apple GPUs.
+- **Portability**: ExaModels.jl can evaluate derivatives on GPU accelerators. The code is currently only tested for NVIDIA GPUs, but GPU code is implemented mostly based on the portable programming paradigm, [KernelAbstractions.jl](https://github.com/JuliaGPU/KernelAbstractions.jl). In the future, we are interested in supporting Intel, AMD, and Apple GPUs.
 
 ## Quick Start
 ### Installation